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.

  1. 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?
  2. 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.

Practices for Applying Industrial Artificial Intelligence, Industry TodayDerick 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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