Cognitive Analytics for Asset Performance Management

Industrial companies increasingly compete in markets affected by globalization and rapid technology advancements. To survive and thrive, these companies are attempting to leverage massive amounts of data from connected and intelligence systems and devices. The phenomena are commonly referred to as the Industrial Internet of Things (IIoT).

Using interconnected equipment, devices and systems, organizations can collect a wealth of real-time data about their operations. Historic data, collected then often unused for decades, can also be mined for new value. Provided the proper methods are used, unstructured data such as video, audio, work logs, manuals, paper work order documents can also be integrated into analytics to provide value.

Analytics is hardly a new thing, as many industrial businesses have been employing historic performance monitoring and describe/discover analysis (also known as “business intelligence”), for some time. However, analytics are quickly evolving beyond their business intelligence roots.

Advanced analytics solutions, particularly those that use machine learning and pattern recognition to predict asset failures, are becoming widely available in industrial markets. Sometimes real-time condition monitoring is also labeled as asset failure prediction. In other contexts, it can be considered as a subset of asset performance management (APM), which ARC has written about extensively in the past.

This report will discuss asset failure prediction within the overall context of predictive analytics. It will outline why predicting asset failures is critical to industrial companies. It will further define and discuss cognitive analytics, a method used to predict asset failure and detail what operational conditions are specifically suited for this application.

In this report, we’ll explain how cognitive analytics differ from other predictive methods. Our goal is to provide some clarity about how to consider cognitive methods within the overall context of APM in your operations.

Table of Contents

• Executive Overview

• Importance of Asset Failure Prediction

• A Recent Resurgence

• Distinguishing Cognitive Methods

• Applying Cognitive Analytics to Predict Asset Failures

• Recommendations

   

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