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What can healthcare teach industrial folks?

7 Best Practices for Applying Industrial Artificial Intelligence Bots

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

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.

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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.


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.


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.




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,

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,

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.