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How IIoT platforms with AR/VR help OEMs reduce operating costs

What can healthcare teach industrial folks?

Deploying an AI-driven IoT platform: 21 questions to ask

How IIoT platforms with AR/VR help OEMs reduce operating costs

Kurt from our Houston office recently visited an upstream operation in Eagle Pass, Texas. At this operation, there was a variety of mission-critical equipment operating and collecting crucial production data points. It took Kurt a good six hours to get the facility. It was time-consuming, tedious and it cost money. We were asking ourselves a simple question: “How can we reduce Kurt’s visits to Eagle Pass by combining the 3D immersive experience of a virtual reality (VR) tool with the deep advanced analytical capabilities of an IIoT platform?” That question led to the development of augmented reality (AR)/VR apps that gracefully compliment an IIoT system.

Take, for example, a pump or a motor that commonly powers upstream operations. Our IIoT platform’s anomaly detection algorithms flag and mark cases of motor temperature overheating. These anomaly markers are laid out on a 3D model of the asset and reliability engineers, one sitting in Houston and another sitting in Oslo, can experience the unhealthy motor from the comfort of their headquarters. The sensor streams from the motor are streamed from historian tags in real time to the IIoT platform. The IIoT platform is then integrated with the AR/VR app, which enables the engineers to perform multiple asset examination operations. They can get “exploded” and “zoomed in” views of the asset and can rotate the asset across the 3D axis to pinpoint what is going wrong and where it’s going wrong.

In addition to experiencing the asset the reliability, engineers at headquarters can use voice and hand-based gestures to understand the sequence of events leading up to a high-value failure mode.

These features are extremely useful for optimizing upstream operations, reducing trips and shaving off costs in a hyper-competitive marketplace. As Harvey Firestone said, “Capital isn’t so important in business. Experience isn’t so important. You can get both these things. What is important is ideas. If you have ideas, you have the main asset you need, and there isn’t any limit to what you can do with your business and your life.” These new ideas promise to change the way OEM and operators run their upstream operations.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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What can healthcare teach industrial folks?

Submitted by Derick Jose on Mon, 08/07/2017 - 11:02

The industrial world (oil and gas, utilities, refineries, discrete/continuous manufacturing) is in the early stages of radical transformation powered by artificial intelligence (AI). AI promises to bring unprecedented efficiencies and competitive advantages by radically transforming. business models. As the race for AI-powered transformation accelerates in the coming years, it would be wise to learn from experiences of the healthcare industry, which experienced a similar trajectory.

Example 1: Anomaly detection in sensor streams

Patients suspected of having arrhythmia will often get an electrocardiogram (ECG) in a doctor's office. However, if an in-office ECG does detect a problem, the doctor prescribes to the patient a wearable ECG that monitors the heart continuously for two weeks. The resulting heartbeat data is then forensically examined (second by second) for any indications of problematic arrhythmias, some of which are extremely difficult to differentiate from harmless heartbeat irregularities. AI algorithms powered by deep-learning techniques can detect 13 types of arrhythmia from ECG signals, helping doctors detect and treat heart problems and extend human life.

How is this applied to the industrial sector? We have been working to detect unusual “electro-mechanical” rhythms in sensor data from variety of upstream assets (like frac pumps sectors), thereby diagnosing the presence of specific fault modes such as impending lube-oil issues or gearbox issues, and extending asset life

Example 2: Diagnostic image detection

Diabetic retinopathy (DR) is the fastest growing cause of blindness and more than 415 million diabetic patients are at risk worldwide. If caught early, the disease can be treated; if not, it can lead to irreversible blindness. Unfortunately, medical specialists capable of detecting the disease are not available in many parts of the world where diabetes is graphic for blog postprevalent. Deep-learning algorithms examine pictures of the back of the eye and rate them for disease presence and severity. Severity is determined by the type of lesions present (e.g. microaneurysms, haemorrhages, hard exudates, etc.), which are indicative of bleeding and fluid leakage in the eye.

How can this be applied to the industrial sector? We are seeing an increased adoption of drones for pipeline inspection in midstream and downstream parts of the oil and gas business. These drones generate terabytes of images scanned by AI algorithms to detect the presence of leaks and fractures. AI can definitely see things the human eye misses in images.

Just getting started? Find our IIoT Launch Template right here

Example 3: “Diagnostic bot in your pocket”

How does having a “doctor in your pocket” feel? That’s precisely what healthcare start-ups are doing by cutting down on unnecessary consultations and developing AI that can engage patients just like a physician. The use of bots by healthcare AI companies (HealthVault, Babylon Health and Medwhat) to diagnose health conditions is increasingly popular.

How is this applied to the industrial sector? We are developing AI bots that can run diagnostic tests on sensor data and highlight the presence of poor quality lube oil, asset misuse on rigs, vibration anomalies and more. These insights can reduce logistic costs associated with troubleshooting remote assets in an industry where the price of a barrel dictates which companies survive and which go out of business.

The healthcare industry is a front-runner in applying AI to mission-critical tasks, whether they be image-detection, anomaly-detection, sequence-detection or bots to automate diagnosis. Industrial AI practitioners can learn a great deal from the healthcare successes and failures, and apply those learnings in the industrial sector.

Jack Welch aptly said, “If the rate of change on the outside exceeds the rate of change on the inside, the end is near.” It’s important for industrial companies to ask the question “Is the rate of change outside greater than that inside?”

Derick Jose is co-founder and chief data scientist at Flutura Decision Sciences and Analytics.

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Deploying an AI-driven IoT platform: 21 questions to ask

My experience with Fortune 100 global energy, engineering, and OEM companies, tells me that a tectonic shift is happening in the energy industry, a shift that promises to change the game in the marketplace forever, leaving the traditional asset and Capex-based business models behind. Increasingly, we are seeing that AI-driven IoT platforms are becoming the digital nervous systems of 21st century industrial companies. IoT platforms are going to be the foundation on which new business models are going to be created — powering new revenue pools and expanding the engineering organization’s foray into other value-added services that bring predictable revenue streams. As a result, the choice of an AI-driven IoT platform is an extremely strategic one which cannot be reversed easily.

As the engineering world collides with the digital world, there is a great deal of confusion and our team felt that more than finding answers, the right questions needed to be asked. Having been soaked in the AI and industrial IoT world, we would like to share a list of 21 mutually exclusive and collectively exhaustive questions spanning core dimensions in applying AI to industrial context.

Instrumenting asset blind spots 

In order to assess the scope of the work, one of the initial tasks at hand is to figure out the “machine learnability” quotient of the asset. Most electromechanical assets have rudimentary instrumentation and may not have the sensors required to capture information in order to model the asset. In order to get context of the remote asset, here are a few questions that reveal the instrumentation and asset landscape:

1.What events are being emitted by the asset today?
2.What events are not being broadcasted by the asset that need to be instrumented or “sensor enabled” going forward for the AI algorithm to learn from?

Sensor health monitoring

One of the most common issues faced in the rugged industrial context is the malfunctioning of sensors which can result in corrupt data being fed to the AI algorithms. As there are hundreds and thousands of assets and sensors, it is very important to know what percentage of the assets and sensors are transmitting healthy sensor data. Basically, we need to look for the absence of events from assets of interest. For example, some sensors had battery issues and were not transmitting:

3.Does the AI-driven IoT platform have dashboards that reveal the number of sensors not broadcasting state information?
4.Do the sensor health monitoring dashboards reveal the length of time that an asset has not been communicating?
5.Does the sensor health monitoring dashboard flag events with spurious data or incorrect data?

AI-driven signal detection

AI is where deep mathematics meets machines; AI and deep learning algorithms crawling in search of patterns to predict asset downtime, asset failure, and asset optimization:

6.Which AI algorithms need a data scientist to configure, and which algorithms can be executed by an asset engineer?
7.Can the AI platform signal anomalies in real time?
8.Can the AI platform express the taxonomy of anomalies experienced by an asset?
9.Can the AI platform correlate the anomalies to asset outcomes (downtime, remaining useful life) that need to be modeled?
10.Can the AI platform have multiple models blended together as an ensemble?
11.Can the AI platform predict in real time or is the prediction in offline mode?

Industrial data product creation   

Industrial data products are a set of AI solvers for real-world business problems. The apps can answer a correlation question or trigger an action signal. As engineers start layering intelligence over their assets using data products, here are a few questions that can help:

12.Can the IoT platform guide users to create edge data products using APIs or using workflows?
13.Can the IoT platform create forensic data products that go beyond “dot on the map” to identify interesting correlations not ever seen before?
14.Can the IoT platform triangulate signals across heterogeneous data pools, sensor historian data streams, maintenance events, ambient asset conditions and other data streams?

Scalability of sensor event streams 

The industrial IoT world will absolutely generate many more events than the consumer world. Take for example the Bombardier C-Series jetliner with Pratt & Whitney’s engine which has 5,000 sensors embedded within it. During a 12-hour flight, 10 GB of data per second is emitted, resulting in 844 TB of data. The scale required for data ingestion is infinitely higher. With that in mind here are a few questions on scalability:

15.What is the peak emission rate of my asset events? Is it thousands per hour, millions per hour?
16.What is the peak ingestion rate of the IoT platform?
17.How much time will it take for an alarm event to reach the central command center? Is it milliseconds or seconds or minutes?

Pricing model of AI-driven IoT applications

The industry is in the early stages of its evolution and multiple pricing modelsexist. Over a period of time, depending upon the complexity of industrial process and its linkage to a financial outcome, the pricing model will eventually stabilize. In the meantime, here are a few questions to ask:

18.Should pricing be set per asset or asset type?
19.Should pricing be set per app or cluster of apps?
20.Event volume-based pricing offered by players like Splunk?
21.Outcome-based pricing like pay per thrust in aviation engines?

Closing thoughts 

With all the considerations above, the choice of an industrial AI-driven IoT platform for assets is a multidisciplinary affair requiring three lenses to look through: the financial lens, the engineering lens and the software lens. Taking the time to consider all of these variables before you begin down the AI path is critical, but can make the task a lot less risky.

Albert Einstein once said, “We cannot solve our problems with the same thinking we used when we created them.” We hope the above questions serve as an actionable AI playbook as you plan out your strategy for an industrial IoT initiative.

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7 Best Practices for Applying Industrial Artificial Intelligence Bots

Marco Polo and seven monetizable IoT intelligence use cases

OEMs, deep learning & IoT powering new biz models

7 Best Practices for Applying Industrial Artificial Intelligence Bots

7 Best Practices for Applying Industrial Artificial Intelligence Bots

As experienced industrial employees leave the workforce, AI bots are filling in the gaps they leave behind. Here are some best practices.

Bots and virtual diagnostic agents are increasingly entering into our daily habits and helping with intermediate tasks which were typically human driven. For example, when we shop online, a conversation bot is activated to understand our purchase intent and, depending on our inputs, a recommendation bot suggests possible items to purchase. Another example is from the health care industry – Flow Health, a start-up, has AI bots that diagnose potential health conditions before a person actually sees the doctor. The consumer

World is slowly being taken over by transaction automating bots.

At Flutura, we have been focussed on applying AI bots to industrial context – for example e have bots facilitate reliability engineering and maintenance diagnostic tasks. Based on our experiences, here are 7 key best practices for applying AI bots in a practical way.

Best Practice-1: Map the high impact tasks

How do you begin your journey to introduce AI bots? It all starts with identifying one high value task.

Which tasks are to be “botified”?

For example in upstream oil and gas, a field service engineer regularly diagnoses irregularities in pumps, motors, winch drives etc. when the arise. Which is a high value failure mode worth diagnosing with a bot instead?

What is the frequency of execution of tasks?

For example a bot may be redundant in identifying potential tasks whereas a mud pump, which is used in harsh conditions, may be down more frequently.

Best Practice-2: Target a measurable operational outcome

Flutura was working with a leading industrial chemical manufacturer where they experienced $16 million worth of reactor downtime caused by valves behaving poorly. An AI powered valve diagnostic bot now helps the company spot valves with a poor health score and recommends the next best action. This bot is expected to bring down the economic impact of downtime by 40%.

Best Practice-3: Decoding users intent from free text using classifier models

One of the primary tasks of the AI bot is to infer the user's intent. For example, based on the query text, does the company want to prioritize the alarms to respond to anomalies or should a root cause analysis of events leading to equipment's failure mode be conducted instead? The microservices decoding the user intent should be robustly tested so that the industrial engineers' experience in the interaction is optimal.

Best Practice-4: AI Bot Integration to adjacent operational systems

Flutura in building a diagnostic AI bot for rod pumps. These do not exist in isolation. The diagnositc needs to “listen” to alarms generated from electronic condition monitoring systems and other data loggers and have the ability to automatically rise a ticket alerting to the potential anomaly.

Best Practice-5: Bot integration with AR/VR applications for collaborative trouble shooting

One of the best use cases to reduce operational cost for upstream oil and gas is collaborative remote trouble shooting in an effort to reduce rig visits. For example, a maintenance engineer sitting in Houston can collaborate with a reliability specialist sitting in Norway by wearing a virtual reality headset. They can dig deep into the real-time MWD (measurement while drilling) logs of a drill bit in Saudi Arabia. This ability to globally collaborate reduces the operational cost associated with expensive rig visits and increases first time resolution of trouble tickets. In this context, Cerebra's diagnostic AI bots are integrated with remote VR/AR apps from Metaverse and provide an immersive three dimensional asset experience to the maintenance /reliability community.

Best Practice-6: Intermediating interbot conversation

The AI bot architecture must accommodate interactions between bots. For example, a diagnostic bot specialized in isolating issues with a frac pump should be able to interact with a cementing truck diagnostic bot as both assets are related in the real world upstream process.

Best Practice-7: Context sensitivity

As the AI diagnostic bot interacts with a reliability engineer for upstream assets, it needs to maintain the context state in which the interactions occur. The context state could be driven by the well where the operation is taking place, the actual asset ID being diagnosed and the relationship this asset has to ambient context and the operator running the asset. This ensures the diagnosis is related to engineering efficiency or operator handling. Closing thoughts

As more and more of the experienced work force leaves the industry, it's necessary to digitally codify trouble shooting best practices from years of experience in solving high value assets failures. It is also important to bring down operational costs associated with remote trouble shooting. Emily Greene famously said “The future will be determined in part by happenings that it is impossible to foresee; it will also be influenced by trends that are now existent and observable.” We at Flutura believe that AI bots combined with AR/VR are the future of industrial operations and we are ready to execute with that in mind.

Closing thoughts

As more and more of the experienced work force leaves the industry, it's necessary to digitally codify trouble shooting best practices from years of experience in solving high value assets failures. It is also important to bring down operational costs associated with remote trouble shooting. Emily Greene famously said “The future will be determined in part by happenings that it is impossible to foresee; it will also be influenced by trends that are now existent and observable.” We at Flutura believe that AI bots combined with AR/VR are the future of industrial operations and we are ready to execute with that in mind.

Derick JoseDerick Jose- Co-Founder and Chief Data Scientist, Flutura Decision Sciences and Analytics
Derick is the Co-Founder & Chief Data Scientist at Flutura and has been in the analytics space for close to 3 decades. Derick oversaw the evolution of data science and is one of its chief architects. Derick career has brought him into many organizations, helping them define the vision for their data monetization programs. Derick is currently developing game changing data products in the industrial IoT space to support disruptive business models for the energy and engineering industry.
Prior to founding Flutura, Derick was Vice President, Knowledge Services at Mindtree and was the part of an elite team that architected the world's largest citizen biometric and demographic data infrastructure.

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Marco Polo and seven monetizable IoT intelligence use cases

In the 13th century, Marco Polo set out with his father and uncle on a great voyage across uncharted territories. They traveled across the vast continent of Asia and became the first Europeans to visit the Chinese capital. For 17 years, Marco Polo explored many parts of world before finally returning to Venice. He later wrote about and mapped out his experiences, inspiring a host of new adventurers and explorers to travel to the exotic lands of the East.

We are all on a voyage similar to Marco Polo’s, navigating the uncharted ocean of IoT big data — seeking those elusive use cases. As we navigate this complex ocean of industrial IoT data, we need two things:

  1. Maps (industry-specific use cases)
  2. Meta patterns (common across industries)

These would help other “Data Marco Polos” avoid the potential minefields we have encountered.

We have abstracted and distilled common big data use cases in industrial IoT that pass the business case test. These are based on real-world projects executed across energy and heavy engineering industries in the U.S. and Japanese markets. Here are the seven core IoT big data use cases that we mapped out:


1. Creating new IoT business models
We worked with a customer that used our IIoT big data technology to restructure the pricing model of field assets based on ultra-specific usage behavior. Before adopting the IIoT analytics product, the customer had a uniform price point for each asset. Deploying the IoT analytics technology helped them transition from a uniform pricing model to executing usage-based dynamic pricing that resulted in improved profitability.

2. Minimize defects in connected plants
The client was a process manufacturing plant located in the Midwest, manufacturing electrical safety products. The quality of its electrical safety product could mean life or death for folks working in the power grid. This customer had sufficiently digitized the manufacturing process to get a continuous real-time stream of humidity, fluid viscosity and ambient temperature conditions. We used this new, rich sensor data pool to identify drivers of defect density and minimize them.

3. Data-driven field recalibration
Many assets come with default factory settings which are not recalibrated resulting in suboptimal performance. We worked with an industrial giant charged with shipping a crucial engineering asset to stabilize the power grid. These assets were constantly inserted into the network ecosystem with default parameter settings. One powerful question we asked was, “Which specific parameter settings discriminate the failed assets from the assets performing well?” Discriminant analysis revealed the parameter settings that needed to be recalibrated along with the optimal band setting. By putting this simple intervention in place, we were able to dramatically impact the number of failure events in the system.

4. Real-time visual intelligence
This is probably the most widely adopted use case, where the platform answers the simple question of “How are my assets doing right now?” This could be transformers in a power grid, oil field assets in a digital oil field context or boilers deployed in the connected plants context. The ability to have real-time “eyes” on industrial field assets streaming in timely state information is crucial. The reduced latency combined with the visual processing of out-of-condition events using geospatial and time-series constructs can be liberating for hardcore engineering industries not used to experiencing the power of real-time field intelligence.

5. Optimizing energy and fuel consumption
For many moving assets like aircraft, fleet trucks and ships, fuel cost is a significant line item in operations. Cost sensor data mashed with location data collected from mobile assets can help optimize fuel efficiency. We worked with a major fleet owner to reduce fuel consumption by 2%, which led to millions of dollars being shaved off the company’s operational expenses. The customer was able to reallocate the funds to a major project it had been putting off due to budget constraints.

6. Asset forensics
As assets become increasingly digitized, businesses can get a granular, 360-degree view of their health spanning sensor data pools, ambient conditions, maintenance events and connected assets. One can confirm an asset failure hypothesis and detect correlations from these new rich data pools. This would be much richer intelligence than the current existing processes would provide today to diagnose asset health.

7. Predicting failure
Once there is a critical mass of signals, multivariate models can be built for scoring an asset on failure probability. Once this predictive failure probability crosses a certain threshold, it can automatically trigger a proactive ticket in the maintenance system (like Maximo or other systems) for an intervention, such as replacing a part, recalibration of a machine or an examination of a machine for closer inspection. Many companies are looking towards predictive maintenance models versus time-series-based maintenance programs to be more efficient in their operations. We have a customer that was able to restructure its entire maintenance program based around real-time streaming signals from its machines. This company has been able to provide a more efficient maintenance program for its customers based on the actual performance of the equipment.

As Marcel Proust said, “The voyage of discovery is not in seeking new landscapes, but in having new eyes.”

Good luck with your IoT big data voyage!

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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OEMs, deep learning & IoT powering new biz models

Original equipment manufacturers (OEMs) are increasingly turning to predictable, recurrent digital services to reveal new revenue streams. Let’s take a look at three business models and how digital learning can help reduce time to solve issues and while increasing revenue.

Model 1: Remote Diagnostics as a Service

I have worked with a leading oil and gas OEM in Houston that had a vision flutura to create new digital revenue streams. The first service that resonated with the oil-field services companies operating the assets was monitoring. They wanted to reduce non-productive downtime. With the increasing footprint of sensors, this model is being extended to additional upstream assets like downhole drill bits, fracking pumps, top drives, and rod pumps.

Model 2: Performance Benchmarking as a Service

I have worked with an OEM that benchmarked the health of the assets deployed, and depending upon their condition, offered additional value-added services, such as finding a buyer for assets past their performance. Performance Benchamarking as a Service is still at the infant stage and we expect this trend to rapidly accelerate in the coming years.

OEM Digital Business Models

Model 3: Extreme Pricing Personalization

In the automotive industry, Progressive Insurance created an offering around bartering machine data (mileage, braking, turns, acceleration) from cars. This data is used as an example for driving habits and was provided in return for discounted insurance prices. Progressive agreed to install a device in the car that would tap into the machine data generated, which would then infer driving habits. The data was used to create risk profiles that informed pricing models unique to the individual, as opposed to being a part of a generic segment.
These examples illustrate how the additional information gleaned from deep learning provides new perspectives on myriad situations, offering new levels of business insights. Deep learning is the new frontier for business to truly begin understanding how to mitigate risk and find new pools of revenue.

by  Derick Jose, Flutura co-founder and chief data scientist. 

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Three practical applications of deep learning and IoT in oil and gas

Flarrio

Three practical applications of deep learning and IoT in oil and gas

Three practical applications of deep learning and IoT in oil and gas

Deep learning and IoT are two game-changing technologies that have the potential to revolutionize the stakes for oil and gas companies facing profitmaking pressure in the face of the dramatic drop in price of oil. In this blog, based on Flutura’s extensive experience in the oil and gas industry, we have highlighted three practical use cases, from the trenches, where these technologies are practically applied to solve real-life problems and impact meaningful business outcomes.

1. Deep learning algorithms detect risks in oil pipelines

In our first use case, we take a look at how algorithms can reveal patterns and information not easily seen in other ways. For instance, drones are increasingly being used for pipeline inspections. As these drones fly through a pipeline, they record an enormous amount of video footage. It’s very difficult for a human being to detect risks such as leaks and cracks in a pipeline. Deep learning algorithms can automatically detect pixel signatures from drone footage for cracks and leaks that humans can miss, thereby minimizing infrastructure risk.

2. Deep learning algorithms detect asset behavior anomalies

While working with several oil and gas companies, we were able to collect a great deal of data from sensors strapped onto upstream assets like frack pumps and rod pumps. Looking for anomalies in high-velocity time-series parameters is like looking for a needle in a haystack for mere mortals. Deep learning algorithms can “see” anomalies that traditional rule-based electronic condition monitoring systems miss and can alert rig operations command centers.

3. Rig diagnostic bots

While providing remote diagnostic services to industrial assets, the conventional form of interaction is through traditional dashboard communications. With the advent of natural language processing algorithms powered by deep learning, field technicians can interact with the asset diagnostic applications through voice interactions just as bots help in customer service.

Concluding thoughts

The advent of deep learning and IoT has brought about great strides in learning, such as predicting and determining attributes, including insights on anomalism, digital signatures, and acoustic changes and patterns. Being able to see beyond what can be seen provides the potential, as is illustrated in our uses cases, to head off potential problems and structural failings, saving organizations time and money and keeping all that benefit from their services safer. We envision a future where the twin digital capabilities of deep learning and IoT will differentiate the winners from the laggards in the competitive energy marketplace — and the first steps are being taken right now.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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Flarrio

Pavel Romashkin, Volitant AI

The future state of the AI technology in IoT is complete, efficient automation. AI will learn to use industrial equipment better than humans and, as a result, replace human operators.

Derick Jose, Flutura

Industrial companies in energy & engineering sector are trying to find practical applications on the ground for AI & IoT which are impacting a measurable financial outcome and running early adopter pilots

Connell McGill, Enertiv

The future of AI in IoT is a world filled with so much data that we can know exactly what is going on everywhere at once and optimize the general state of things. Kind of like a human nervous system, but for the entire planet.

Sastry Malladi, FogHorn

AI is rapidly penetrating Edge Computing, particularly in IIoT. Analytics and Machine Learning is already prevalent in Edge devices and the next logical step is AI to further optimize processes.

Nelson Chu, Parametric

Soon, AI will automate routines for IoT. For example, when you turn on your lights and TV together, it will create scenes for automation. Additionally, AI could use sensors to generate shopping lists.

Rich Rogers, Hitachi

In the last century, electricity fueled the industrial revolution, giving us powerful factories and machines. Today, IIoT and AI software are bringing them to life in new and unexpected ways.

Mahi de Silva, Botworx.ai

AI is already being integrated into IoT and even IIoT, where the machines and products are able to diagnose themselves and interact with their human operators.

Jeremy Pola, Novecom

The future of IoT and IIoT is in manufacturing user experiences that deliver advanced analytics and data visualisation. This will be achieved through collaboration of computer science and data science.

Nenad Cuk, CroatiaTech.com

I see AI systems in the near future controlling, navigating and maintaining IoT devices and products. One category in particular, drones and how they are managed. With thousands of drones in the sky, AI will need to carry the weight and manage these systems on a grand scale.

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How IIoT platforms with AR/VR help OEMs reduce operating costs

How IIoT platforms with AR/VR help OEMs reduce operating costs

Kurt from our Houston office recently visited an upstream operation in Eagle Pass, Texas. At this operation, there was a variety of mission-critical equipment operating and collecting crucial production data points. It took Kurt a good six hours to get the facility. It was time-consuming, tedious and it cost money. We were asking ourselves a simple question: “How can we reduce Kurt’s visits to Eagle Pass by combining the 3D immersive experience of a virtual reality (VR) tool with the deep advanced analytical capabilities of an IIoT platform?” That question led to the development of augmented reality (AR)/VR apps that gracefully compliment an IIoT system.

Take, for example, a pump or a motor that commonly powers upstream operations. Our IIoT platform’s anomaly detection algorithms flag and mark cases of motor temperature overheating. These anomaly markers are laid out on a 3D model of the asset and reliability engineers, one sitting in Houston and another sitting in Oslo, can experience the unhealthy motor from the comfort of their headquarters. The sensor streams from the motor are streamed from historian tags in real time to the IIoT platform. The IIoT platform is then integrated with the AR/VR app, which enables the engineers to perform multiple asset examination operations. They can get “exploded” and “zoomed in” views of the asset and can rotate the asset across the 3D axis to pinpoint what is going wrong and where it’s going wrong.

In addition to experiencing the asset the reliability, engineers at headquarters can use voice and hand-based gestures to understand the sequence of events leading up to a high-value failure mode.

These features are extremely useful for optimizing upstream operations, reducing trips and shaving off costs in a hyper-competitive marketplace. As Harvey Firestone said, “Capital isn’t so important in business. Experience isn’t so important. You can get both these things. What is important is ideas. If you have ideas, you have the main asset you need, and there isn’t any limit to what you can do with your business and your life.” These new ideas promise to change the way OEM and operators run their upstream operations.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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What can healthcare teach industrial folks?

What can healthcare teach industrial folks?

Submitted by Derick Jose on Mon, 08/07/2017 - 11:02

The industrial world (oil and gas, utilities, refineries, discrete/continuous manufacturing) is in the early stages of radical transformation powered by artificial intelligence (AI). AI promises to bring unprecedented efficiencies and competitive advantages by radically transforming. business models. As the race for AI-powered transformation accelerates in the coming years, it would be wise to learn from experiences of the healthcare industry, which experienced a similar trajectory.

Example 1: Anomaly detection in sensor streams

Patients suspected of having arrhythmia will often get an electrocardiogram (ECG) in a doctor's office. However, if an in-office ECG does detect a problem, the doctor prescribes to the patient a wearable ECG that monitors the heart continuously for two weeks. The resulting heartbeat data is then forensically examined (second by second) for any indications of problematic arrhythmias, some of which are extremely difficult to differentiate from harmless heartbeat irregularities. AI algorithms powered by deep-learning techniques can detect 13 types of arrhythmia from ECG signals, helping doctors detect and treat heart problems and extend human life.

How is this applied to the industrial sector? We have been working to detect unusual “electro-mechanical” rhythms in sensor data from variety of upstream assets (like frac pumps sectors), thereby diagnosing the presence of specific fault modes such as impending lube-oil issues or gearbox issues, and extending asset life

Example 2: Diagnostic image detection

Diabetic retinopathy (DR) is the fastest growing cause of blindness and more than 415 million diabetic patients are at risk worldwide. If caught early, the disease can be treated; if not, it can lead to irreversible blindness. Unfortunately, medical specialists capable of detecting the disease are not available in many parts of the world where diabetes is graphic for blog postprevalent. Deep-learning algorithms examine pictures of the back of the eye and rate them for disease presence and severity. Severity is determined by the type of lesions present (e.g. microaneurysms, haemorrhages, hard exudates, etc.), which are indicative of bleeding and fluid leakage in the eye.

How can this be applied to the industrial sector? We are seeing an increased adoption of drones for pipeline inspection in midstream and downstream parts of the oil and gas business. These drones generate terabytes of images scanned by AI algorithms to detect the presence of leaks and fractures. AI can definitely see things the human eye misses in images.

Just getting started? Find our IIoT Launch Template right here

Example 3: “Diagnostic bot in your pocket”

How does having a “doctor in your pocket” feel? That’s precisely what healthcare start-ups are doing by cutting down on unnecessary consultations and developing AI that can engage patients just like a physician. The use of bots by healthcare AI companies (HealthVault, Babylon Health and Medwhat) to diagnose health conditions is increasingly popular.

How is this applied to the industrial sector? We are developing AI bots that can run diagnostic tests on sensor data and highlight the presence of poor quality lube oil, asset misuse on rigs, vibration anomalies and more. These insights can reduce logistic costs associated with troubleshooting remote assets in an industry where the price of a barrel dictates which companies survive and which go out of business.

The healthcare industry is a front-runner in applying AI to mission-critical tasks, whether they be image-detection, anomaly-detection, sequence-detection or bots to automate diagnosis. Industrial AI practitioners can learn a great deal from the healthcare successes and failures, and apply those learnings in the industrial sector.

Jack Welch aptly said, “If the rate of change on the outside exceeds the rate of change on the inside, the end is near.” It’s important for industrial companies to ask the question “Is the rate of change outside greater than that inside?”

Derick Jose is co-founder and chief data scientist at Flutura Decision Sciences and Analytics.

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Deploying an AI-driven IoT platform: 21 questions to ask

Deploying an AI-driven IoT platform: 21 questions to ask

My experience with Fortune 100 global energy, engineering, and OEM companies, tells me that a tectonic shift is happening in the energy industry, a shift that promises to change the game in the marketplace forever, leaving the traditional asset and Capex-based business models behind. Increasingly, we are seeing that AI-driven IoT platforms are becoming the digital nervous systems of 21st century industrial companies. IoT platforms are going to be the foundation on which new business models are going to be created — powering new revenue pools and expanding the engineering organization’s foray into other value-added services that bring predictable revenue streams. As a result, the choice of an AI-driven IoT platform is an extremely strategic one which cannot be reversed easily.

As the engineering world collides with the digital world, there is a great deal of confusion and our team felt that more than finding answers, the right questions needed to be asked. Having been soaked in the AI and industrial IoT world, we would like to share a list of 21 mutually exclusive and collectively exhaustive questions spanning core dimensions in applying AI to industrial context.

Instrumenting asset blind spots 

In order to assess the scope of the work, one of the initial tasks at hand is to figure out the “machine learnability” quotient of the asset. Most electromechanical assets have rudimentary instrumentation and may not have the sensors required to capture information in order to model the asset. In order to get context of the remote asset, here are a few questions that reveal the instrumentation and asset landscape:

1.What events are being emitted by the asset today?
2.What events are not being broadcasted by the asset that need to be instrumented or “sensor enabled” going forward for the AI algorithm to learn from?

Sensor health monitoring

One of the most common issues faced in the rugged industrial context is the malfunctioning of sensors which can result in corrupt data being fed to the AI algorithms. As there are hundreds and thousands of assets and sensors, it is very important to know what percentage of the assets and sensors are transmitting healthy sensor data. Basically, we need to look for the absence of events from assets of interest. For example, some sensors had battery issues and were not transmitting:

3.Does the AI-driven IoT platform have dashboards that reveal the number of sensors not broadcasting state information?
4.Do the sensor health monitoring dashboards reveal the length of time that an asset has not been communicating?
5.Does the sensor health monitoring dashboard flag events with spurious data or incorrect data?

AI-driven signal detection

AI is where deep mathematics meets machines; AI and deep learning algorithms crawling in search of patterns to predict asset downtime, asset failure, and asset optimization:

6.Which AI algorithms need a data scientist to configure, and which algorithms can be executed by an asset engineer?
7.Can the AI platform signal anomalies in real time?
8.Can the AI platform express the taxonomy of anomalies experienced by an asset?
9.Can the AI platform correlate the anomalies to asset outcomes (downtime, remaining useful life) that need to be modeled?
10.Can the AI platform have multiple models blended together as an ensemble?
11.Can the AI platform predict in real time or is the prediction in offline mode?

Industrial data product creation   

Industrial data products are a set of AI solvers for real-world business problems. The apps can answer a correlation question or trigger an action signal. As engineers start layering intelligence over their assets using data products, here are a few questions that can help:

12.Can the IoT platform guide users to create edge data products using APIs or using workflows?
13.Can the IoT platform create forensic data products that go beyond “dot on the map” to identify interesting correlations not ever seen before?
14.Can the IoT platform triangulate signals across heterogeneous data pools, sensor historian data streams, maintenance events, ambient asset conditions and other data streams?

Scalability of sensor event streams 

The industrial IoT world will absolutely generate many more events than the consumer world. Take for example the Bombardier C-Series jetliner with Pratt & Whitney’s engine which has 5,000 sensors embedded within it. During a 12-hour flight, 10 GB of data per second is emitted, resulting in 844 TB of data. The scale required for data ingestion is infinitely higher. With that in mind here are a few questions on scalability:

15.What is the peak emission rate of my asset events? Is it thousands per hour, millions per hour?
16.What is the peak ingestion rate of the IoT platform?
17.How much time will it take for an alarm event to reach the central command center? Is it milliseconds or seconds or minutes?

Pricing model of AI-driven IoT applications

The industry is in the early stages of its evolution and multiple pricing modelsexist. Over a period of time, depending upon the complexity of industrial process and its linkage to a financial outcome, the pricing model will eventually stabilize. In the meantime, here are a few questions to ask:

18.Should pricing be set per asset or asset type?
19.Should pricing be set per app or cluster of apps?
20.Event volume-based pricing offered by players like Splunk?
21.Outcome-based pricing like pay per thrust in aviation engines?

Closing thoughts 

With all the considerations above, the choice of an industrial AI-driven IoT platform for assets is a multidisciplinary affair requiring three lenses to look through: the financial lens, the engineering lens and the software lens. Taking the time to consider all of these variables before you begin down the AI path is critical, but can make the task a lot less risky.

Albert Einstein once said, “We cannot solve our problems with the same thinking we used when we created them.” We hope the above questions serve as an actionable AI playbook as you plan out your strategy for an industrial IoT initiative.

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7 Best Practices for Applying Industrial Artificial Intelligence Bots

7 Best Practices for Applying Industrial Artificial Intelligence Bots

7 Best Practices for Applying Industrial Artificial Intelligence Bots

As experienced industrial employees leave the workforce, AI bots are filling in the gaps they leave behind. Here are some best practices.

Bots and virtual diagnostic agents are increasingly entering into our daily habits and helping with intermediate tasks which were typically human driven. For example, when we shop online, a conversation bot is activated to understand our purchase intent and, depending on our inputs, a recommendation bot suggests possible items to purchase. Another example is from the health care industry – Flow Health, a start-up, has AI bots that diagnose potential health conditions before a person actually sees the doctor. The consumer

World is slowly being taken over by transaction automating bots.

At Flutura, we have been focussed on applying AI bots to industrial context – for example e have bots facilitate reliability engineering and maintenance diagnostic tasks. Based on our experiences, here are 7 key best practices for applying AI bots in a practical way.

Best Practice-1: Map the high impact tasks

How do you begin your journey to introduce AI bots? It all starts with identifying one high value task.

Which tasks are to be “botified”?

For example in upstream oil and gas, a field service engineer regularly diagnoses irregularities in pumps, motors, winch drives etc. when the arise. Which is a high value failure mode worth diagnosing with a bot instead?

What is the frequency of execution of tasks?

For example a bot may be redundant in identifying potential tasks whereas a mud pump, which is used in harsh conditions, may be down more frequently.

Best Practice-2: Target a measurable operational outcome

Flutura was working with a leading industrial chemical manufacturer where they experienced $16 million worth of reactor downtime caused by valves behaving poorly. An AI powered valve diagnostic bot now helps the company spot valves with a poor health score and recommends the next best action. This bot is expected to bring down the economic impact of downtime by 40%.

Best Practice-3: Decoding users intent from free text using classifier models

One of the primary tasks of the AI bot is to infer the user's intent. For example, based on the query text, does the company want to prioritize the alarms to respond to anomalies or should a root cause analysis of events leading to equipment's failure mode be conducted instead? The microservices decoding the user intent should be robustly tested so that the industrial engineers' experience in the interaction is optimal.

Best Practice-4: AI Bot Integration to adjacent operational systems

Flutura in building a diagnostic AI bot for rod pumps. These do not exist in isolation. The diagnositc needs to “listen” to alarms generated from electronic condition monitoring systems and other data loggers and have the ability to automatically rise a ticket alerting to the potential anomaly.

Best Practice-5: Bot integration with AR/VR applications for collaborative trouble shooting

One of the best use cases to reduce operational cost for upstream oil and gas is collaborative remote trouble shooting in an effort to reduce rig visits. For example, a maintenance engineer sitting in Houston can collaborate with a reliability specialist sitting in Norway by wearing a virtual reality headset. They can dig deep into the real-time MWD (measurement while drilling) logs of a drill bit in Saudi Arabia. This ability to globally collaborate reduces the operational cost associated with expensive rig visits and increases first time resolution of trouble tickets. In this context, Cerebra's diagnostic AI bots are integrated with remote VR/AR apps from Metaverse and provide an immersive three dimensional asset experience to the maintenance /reliability community.

Best Practice-6: Intermediating interbot conversation

The AI bot architecture must accommodate interactions between bots. For example, a diagnostic bot specialized in isolating issues with a frac pump should be able to interact with a cementing truck diagnostic bot as both assets are related in the real world upstream process.

Best Practice-7: Context sensitivity

As the AI diagnostic bot interacts with a reliability engineer for upstream assets, it needs to maintain the context state in which the interactions occur. The context state could be driven by the well where the operation is taking place, the actual asset ID being diagnosed and the relationship this asset has to ambient context and the operator running the asset. This ensures the diagnosis is related to engineering efficiency or operator handling. Closing thoughts

As more and more of the experienced work force leaves the industry, it's necessary to digitally codify trouble shooting best practices from years of experience in solving high value assets failures. It is also important to bring down operational costs associated with remote trouble shooting. Emily Greene famously said “The future will be determined in part by happenings that it is impossible to foresee; it will also be influenced by trends that are now existent and observable.” We at Flutura believe that AI bots combined with AR/VR are the future of industrial operations and we are ready to execute with that in mind.

Closing thoughts

As more and more of the experienced work force leaves the industry, it's necessary to digitally codify trouble shooting best practices from years of experience in solving high value assets failures. It is also important to bring down operational costs associated with remote trouble shooting. Emily Greene famously said “The future will be determined in part by happenings that it is impossible to foresee; it will also be influenced by trends that are now existent and observable.” We at Flutura believe that AI bots combined with AR/VR are the future of industrial operations and we are ready to execute with that in mind.

Derick JoseDerick Jose- Co-Founder and Chief Data Scientist, Flutura Decision Sciences and Analytics
Derick is the Co-Founder & Chief Data Scientist at Flutura and has been in the analytics space for close to 3 decades. Derick oversaw the evolution of data science and is one of its chief architects. Derick career has brought him into many organizations, helping them define the vision for their data monetization programs. Derick is currently developing game changing data products in the industrial IoT space to support disruptive business models for the energy and engineering industry.
Prior to founding Flutura, Derick was Vice President, Knowledge Services at Mindtree and was the part of an elite team that architected the world's largest citizen biometric and demographic data infrastructure.

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Marco Polo and seven monetizable IoT intelligence use cases

Marco Polo and seven monetizable IoT intelligence use cases

In the 13th century, Marco Polo set out with his father and uncle on a great voyage across uncharted territories. They traveled across the vast continent of Asia and became the first Europeans to visit the Chinese capital. For 17 years, Marco Polo explored many parts of world before finally returning to Venice. He later wrote about and mapped out his experiences, inspiring a host of new adventurers and explorers to travel to the exotic lands of the East.

We are all on a voyage similar to Marco Polo’s, navigating the uncharted ocean of IoT big data — seeking those elusive use cases. As we navigate this complex ocean of industrial IoT data, we need two things:

  1. Maps (industry-specific use cases)
  2. Meta patterns (common across industries)

These would help other “Data Marco Polos” avoid the potential minefields we have encountered.

We have abstracted and distilled common big data use cases in industrial IoT that pass the business case test. These are based on real-world projects executed across energy and heavy engineering industries in the U.S. and Japanese markets. Here are the seven core IoT big data use cases that we mapped out:


1. Creating new IoT business models
We worked with a customer that used our IIoT big data technology to restructure the pricing model of field assets based on ultra-specific usage behavior. Before adopting the IIoT analytics product, the customer had a uniform price point for each asset. Deploying the IoT analytics technology helped them transition from a uniform pricing model to executing usage-based dynamic pricing that resulted in improved profitability.

2. Minimize defects in connected plants
The client was a process manufacturing plant located in the Midwest, manufacturing electrical safety products. The quality of its electrical safety product could mean life or death for folks working in the power grid. This customer had sufficiently digitized the manufacturing process to get a continuous real-time stream of humidity, fluid viscosity and ambient temperature conditions. We used this new, rich sensor data pool to identify drivers of defect density and minimize them.

3. Data-driven field recalibration
Many assets come with default factory settings which are not recalibrated resulting in suboptimal performance. We worked with an industrial giant charged with shipping a crucial engineering asset to stabilize the power grid. These assets were constantly inserted into the network ecosystem with default parameter settings. One powerful question we asked was, “Which specific parameter settings discriminate the failed assets from the assets performing well?” Discriminant analysis revealed the parameter settings that needed to be recalibrated along with the optimal band setting. By putting this simple intervention in place, we were able to dramatically impact the number of failure events in the system.

4. Real-time visual intelligence
This is probably the most widely adopted use case, where the platform answers the simple question of “How are my assets doing right now?” This could be transformers in a power grid, oil field assets in a digital oil field context or boilers deployed in the connected plants context. The ability to have real-time “eyes” on industrial field assets streaming in timely state information is crucial. The reduced latency combined with the visual processing of out-of-condition events using geospatial and time-series constructs can be liberating for hardcore engineering industries not used to experiencing the power of real-time field intelligence.

5. Optimizing energy and fuel consumption
For many moving assets like aircraft, fleet trucks and ships, fuel cost is a significant line item in operations. Cost sensor data mashed with location data collected from mobile assets can help optimize fuel efficiency. We worked with a major fleet owner to reduce fuel consumption by 2%, which led to millions of dollars being shaved off the company’s operational expenses. The customer was able to reallocate the funds to a major project it had been putting off due to budget constraints.

6. Asset forensics
As assets become increasingly digitized, businesses can get a granular, 360-degree view of their health spanning sensor data pools, ambient conditions, maintenance events and connected assets. One can confirm an asset failure hypothesis and detect correlations from these new rich data pools. This would be much richer intelligence than the current existing processes would provide today to diagnose asset health.

7. Predicting failure
Once there is a critical mass of signals, multivariate models can be built for scoring an asset on failure probability. Once this predictive failure probability crosses a certain threshold, it can automatically trigger a proactive ticket in the maintenance system (like Maximo or other systems) for an intervention, such as replacing a part, recalibration of a machine or an examination of a machine for closer inspection. Many companies are looking towards predictive maintenance models versus time-series-based maintenance programs to be more efficient in their operations. We have a customer that was able to restructure its entire maintenance program based around real-time streaming signals from its machines. This company has been able to provide a more efficient maintenance program for its customers based on the actual performance of the equipment.

As Marcel Proust said, “The voyage of discovery is not in seeking new landscapes, but in having new eyes.”

Good luck with your IoT big data voyage!

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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OEMs, deep learning & IoT powering new biz models

OEMs, deep learning & IoT powering new biz models

Original equipment manufacturers (OEMs) are increasingly turning to predictable, recurrent digital services to reveal new revenue streams. Let’s take a look at three business models and how digital learning can help reduce time to solve issues and while increasing revenue.

Model 1: Remote Diagnostics as a Service

I have worked with a leading oil and gas OEM in Houston that had a vision flutura to create new digital revenue streams. The first service that resonated with the oil-field services companies operating the assets was monitoring. They wanted to reduce non-productive downtime. With the increasing footprint of sensors, this model is being extended to additional upstream assets like downhole drill bits, fracking pumps, top drives, and rod pumps.

Model 2: Performance Benchmarking as a Service

I have worked with an OEM that benchmarked the health of the assets deployed, and depending upon their condition, offered additional value-added services, such as finding a buyer for assets past their performance. Performance Benchamarking as a Service is still at the infant stage and we expect this trend to rapidly accelerate in the coming years.

OEM Digital Business Models

Model 3: Extreme Pricing Personalization

In the automotive industry, Progressive Insurance created an offering around bartering machine data (mileage, braking, turns, acceleration) from cars. This data is used as an example for driving habits and was provided in return for discounted insurance prices. Progressive agreed to install a device in the car that would tap into the machine data generated, which would then infer driving habits. The data was used to create risk profiles that informed pricing models unique to the individual, as opposed to being a part of a generic segment.
These examples illustrate how the additional information gleaned from deep learning provides new perspectives on myriad situations, offering new levels of business insights. Deep learning is the new frontier for business to truly begin understanding how to mitigate risk and find new pools of revenue.

by  Derick Jose, Flutura co-founder and chief data scientist. 

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Three practical applications of deep learning and IoT in oil and gas

Three practical applications of deep learning and IoT in oil and gas

Three practical applications of deep learning and IoT in oil and gas

Deep learning and IoT are two game-changing technologies that have the potential to revolutionize the stakes for oil and gas companies facing profitmaking pressure in the face of the dramatic drop in price of oil. In this blog, based on Flutura’s extensive experience in the oil and gas industry, we have highlighted three practical use cases, from the trenches, where these technologies are practically applied to solve real-life problems and impact meaningful business outcomes.

1. Deep learning algorithms detect risks in oil pipelines

In our first use case, we take a look at how algorithms can reveal patterns and information not easily seen in other ways. For instance, drones are increasingly being used for pipeline inspections. As these drones fly through a pipeline, they record an enormous amount of video footage. It’s very difficult for a human being to detect risks such as leaks and cracks in a pipeline. Deep learning algorithms can automatically detect pixel signatures from drone footage for cracks and leaks that humans can miss, thereby minimizing infrastructure risk.

2. Deep learning algorithms detect asset behavior anomalies

While working with several oil and gas companies, we were able to collect a great deal of data from sensors strapped onto upstream assets like frack pumps and rod pumps. Looking for anomalies in high-velocity time-series parameters is like looking for a needle in a haystack for mere mortals. Deep learning algorithms can “see” anomalies that traditional rule-based electronic condition monitoring systems miss and can alert rig operations command centers.

3. Rig diagnostic bots

While providing remote diagnostic services to industrial assets, the conventional form of interaction is through traditional dashboard communications. With the advent of natural language processing algorithms powered by deep learning, field technicians can interact with the asset diagnostic applications through voice interactions just as bots help in customer service.

Concluding thoughts

The advent of deep learning and IoT has brought about great strides in learning, such as predicting and determining attributes, including insights on anomalism, digital signatures, and acoustic changes and patterns. Being able to see beyond what can be seen provides the potential, as is illustrated in our uses cases, to head off potential problems and structural failings, saving organizations time and money and keeping all that benefit from their services safer. We envision a future where the twin digital capabilities of deep learning and IoT will differentiate the winners from the laggards in the competitive energy marketplace — and the first steps are being taken right now.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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Flarrio

Flarrio

Pavel Romashkin, Volitant AI

The future state of the AI technology in IoT is complete, efficient automation. AI will learn to use industrial equipment better than humans and, as a result, replace human operators.

Derick Jose, Flutura

Industrial companies in energy & engineering sector are trying to find practical applications on the ground for AI & IoT which are impacting a measurable financial outcome and running early adopter pilots

Connell McGill, Enertiv

The future of AI in IoT is a world filled with so much data that we can know exactly what is going on everywhere at once and optimize the general state of things. Kind of like a human nervous system, but for the entire planet.

Sastry Malladi, FogHorn

AI is rapidly penetrating Edge Computing, particularly in IIoT. Analytics and Machine Learning is already prevalent in Edge devices and the next logical step is AI to further optimize processes.

Nelson Chu, Parametric

Soon, AI will automate routines for IoT. For example, when you turn on your lights and TV together, it will create scenes for automation. Additionally, AI could use sensors to generate shopping lists.

Rich Rogers, Hitachi

In the last century, electricity fueled the industrial revolution, giving us powerful factories and machines. Today, IIoT and AI software are bringing them to life in new and unexpected ways.

Mahi de Silva, Botworx.ai

AI is already being integrated into IoT and even IIoT, where the machines and products are able to diagnose themselves and interact with their human operators.

Jeremy Pola, Novecom

The future of IoT and IIoT is in manufacturing user experiences that deliver advanced analytics and data visualisation. This will be achieved through collaboration of computer science and data science.

Nenad Cuk, CroatiaTech.com

I see AI systems in the near future controlling, navigating and maintaining IoT devices and products. One category in particular, drones and how they are managed. With thousands of drones in the sky, AI will need to carry the weight and manage these systems on a grand scale.

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