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.
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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:
- What events are being emitted by the asset today?
- 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:
- Does the AI-driven IoT platform have dashboards that reveal the number of sensors not broadcasting state information?
- Do the sensor health monitoring dashboards reveal the length of time that an asset has not been communicating?
- 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:
- Which AI algorithms need a data scientist to configure, and which algorithms can be executed by an asset engineer?
- Can the AI platform signal anomalies in real time?
- Can the AI platform express the taxonomy of anomalies experienced by an asset?
- Can the AI platform correlate the anomalies to asset outcomes (downtime, remaining useful life) that need to be modelled?
- Can the AI platform have multiple models blended together as an ensemble?
- 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:
- Can the IoT platform guide users to create edge data products using APIs or using workflows?
- Can the IoT platform create forensic data products that go beyond “dot on the map” to identify interesting correlations not ever seen before?
- 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:
- What is the peak emission rate of my asset events? Is it thousands per hour, millions per hour?
- What is the peak ingestion rate of the IoT platform?
- 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 models exist. 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:
- Should pricing be set per asset or asset type?
- Should pricing be set per app or cluster of apps?
- Event volume-based pricing offered by players like Splunk?
- Outcome-based pricing like pay per thrust in aviation engines?
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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