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Prescriptive Analytics for Industrial IoT Failure Risk Management

industrial IoTRisk Management techniques and enterprise tools have been around for some time, mostly in insurance, finance and banking. With the growth of industrial IoT and connected devices, OEMs and industrial customers have more opportunity to apply similar techniques to industrial equipment failure and maintenance problems. As some of our customers like ABT have shown, such new problems require state of the art Prescriptive Analytics tools to reduce failure risk and optimize maintenance costs in any industrial setting.

There are three obvious reasons the latest analytics tools can improve IoT failure risk management:

1. New, more granular IoT data may be difficult to correlate with failures –

Unlike the financial transactions where human behavior and fraud have been tracked for some time, machine data from newly connected, sub-components and related equipment failures are relatively new. Since there may be limited historical information correlating newly archived data with documented failures, it is essential to augment predictive analytics (machine learning) tools with traditional human experience (business rules).

2. Correlating multiple IoT components with a failure is difficult –

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As the sensors and components become more prevalent, it may be difficult to correlate a particular component behavior to a failure. For example, in a commercial distillery, the increased temperature of a distillate and related loss of alcohol efficiency on a hybrid still may be caused by either reduced coolant flow, blockage of a redistillation plate or a steam valve failure. By tracking sensors on each component and correlating them with human operator experience, a distilling plant can predict a more appropriate cleaning and maintenance of a particular distilling component. In other works, collecting data from multiple industrial IoT components and blending it with experience-based learning will significantly improve predicting likelihood of failure or a need for unscheduled maintenance to maintain equipment efficiency.

3. Learning improves maintenance insight, reduces costs –

power line maintenanceToday, most OEMs have periodic scheduled maintenance whether needed or not. Frequently, such maintenance does not account for higher risk of failure due to a component problem. As a result, industrial customers experience both unnecessary maintenance and unpredictable failures, both which increase costs and prolong costly downtimes. For example, one of our customers, a major power distributor in Western Australia, combined predictive analytics with decision logic to identify power grid components more likely to fail soon.

Modern decision management platforms like Sparkling Logic SMARTS allow improving the ultimate Risk Management problem. They allow evolution of intelligent industrial machinery that learns and suggests failure before and outside scheduled maintenance intervals. I predict that using such tools, progressive OEMs and industrial customers will move to variable maintenance schedules and predict the majority of failures BEFORE they happen.

In summary, industrial customers and industrial equipment OEMs need modern tools to manage connected IoT components and equipment and ultimately implement advanced industrial IoT risk management. These techniques will result in higher uptime, lower maintenance costs and higher productivity. Such modern prescriptive analytics tools provide two key areas of expertise:

  1. Predictive Analytics –
  2. to quickly analyze IoT device data, visualize, predict and learn the patterns of failure and suggest best course of action or improved maintenance schedules.

  3. Decision Management / Rules Engines –
  4. to implement predictive discoveries in an easy, graphical fashion as well as to test multiple failure scenarios and instantly deploy the industrial failure risk logic. Deploying and automating improved failure risk decisions will allow even less skilled operators to manage even most complex industrial systems with great efficiency.

Learn more about how SMARTS Decision Manager can help improve your IoT failure risk.

Learn How IoT Systems Make Real-time Decisions

A recent blog post, by one of our partners, Mariner, describes the high-level architecture they use to implement Internet of Things (IoT) solutions for their manufacturing and distribution customers. Data from devices and sensors is collected, aggregated, and enhanced using the Microsoft Azure suite of technologies and passed to a SMARTS decision service.

IoT system architecture

In this post, we’ll explore how Mariner uses the SMARTS decision management platform, to make real-time decisions based on sensor data.

Customer Example

Let’s look at an actual customer story. ABT Power Management is an innovative power management company based in North Carolina. ABT has a joint product/service offering called GuaranteedPOWER® where they use an IoT solution (developed by Mariner) to proactively manage and maintain the batteries that power lift trucks and other material handling vehicles. It turns out that if the batteries aren’t properly maintained, over time they can actually cost more then the trucks they power. (See the ABT Power Management Customer Story.)

As the high-level architecture diagram illustrates, sensor data from batteries and chargers is collected and enhanced prior to being passed to a SMARTS decision service. The SMARTS decision service alerts ABT to dispatch field engineers to the site when equipment maintenance or repair is needed.

The knowledge on how to properly maintain the batteries and chargers is based on the manufacturers’ recommendations and the expertise of ABT’s field engineers. The engineers have a deep understanding of the technology and years of real-world experience. Mariner worked with the engineers to translate their knowledge into concrete SMARTS decisions that define how to proactively manage and service the batteries and chargers. These decisions are made up of rules that define when to perform specific maintenance services such as charging, watering, and rotating the batteries at a customer’s site.

Capturing the Knowledge

Let’s look at a simple example. One condition the sensors monitor is the water level in the batteries. When the water level is low, the battery needs water. You could write a simple rule to capture this:

IF battery water level is low
THEN create an alert to water the battery

But in the real world, this rule is not quite as simple as it seems at first glance! Battery sensors are very sensitive instruments and when a battery is being physically moved it could register a low water condition. So the rules need to take this knowledge into account and perhaps detect if the battery water level registers low for two or more consecutive days. Also the rules need to consider additional factors in deciding whether or not to water the battery. For example, how many other maintenance actions are required at a site? How much time does it take and how much does it cost to dispatch a field engineer to the site? And, can the maintenance visit be scheduled so that it coincides with maintenance and repair required by other customers who are in the same geographical area?

All of this knowledge is captured in the decision making rules that analyze the sensor data. In addition, predictive analytics is used to detect patterns in the data that could lead to future equipment failures. Preventative maintenance is scheduled to prevent these failures.

Mariner’s business analysts captured and tested these rules and decisions in the SMARTS Analyst Workbench (watch this video to see the rules in SMARTS).

Mariner also used SMARTS to run simulations using historical data in order to ensure that the decisions (and resulting alerts) were consistent with the recommended actions specified by ABT’s field engineers.

Deploying and Continuously Improving

Once tested and validated, the rules were deployed to the SMARTS decision service, where they make proactive maintenance and servicing decisions in real time. Over time, new rules have been added and existing rules have been improved and refined so that more conditions can be automatically detected and acted upon.

In summary, SMARTS Decision Manager provides an ideal platform for automating and deploying IoT real-time decisions. SMARTS helps organizations make sense of vast amounts of sensor data and translate it into concrete actions.

Why Decision Management for the Internet of Things (IoT)?

Internet of ThingsHere is a hint…  Earlier this week I shared my views on why Data was such a significant enabler for Decision Management, and vice versa.

With the Internet of Things, we get surrounded by ‘things’ that we want smarter.  The idea is that devices like switches, batteries and any appliance could make decisions in our stead.  I remember the days when we thought that it was science fiction…  Instead of programming the furnace to turn on at this or that time or temperature, we were dreaming of a house that could adapt, anticipate and react to current events or information.  And now, it’s happening.

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