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Predictive Maintenance and Machine Learning

Machine Learning Creates Added Value

| Author / Editor: Richard Irwin* / Anke Geipel-Kern

Richard Irwin ist Senior Product Marketer for AssetWise Operational Analytics bei Bentley.
Richard Irwin ist Senior Product Marketer for AssetWise Operational Analytics bei Bentley. (Bild: Bentley)

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Machine learning can create the basis for higher plant availability, says Richard Irwin, Senior Product Marketer for Asset Wise at Bentley. But the implementation of such systems must be well-prepared. Only then can real added value be created.

While machine learning has been researched for decades, its use in applying artificial intelligence (AI) in industrial plants and infrastructure asset operations is now advancing at a rapid pace. This influx of using machine learning is due to the growth in big data, the Industrial Internet of Things (IIoT), computing power, and the need for superior predictive and prescriptive capabilities required to manage today’s complex assets.

Principles of Machine Learning

While machine learning has typically been linked with industries such as transportation and banking (think of self-driving cars and fraud monitoring, respectively), there are many uses for machine learning and predictive maintenance within the industrial sector. This article will focus on some of the principles within machine learning and industries that are primed to take advantage of the application of machine learning to maximize the benefits it brings to improve situational intelligence, performance, and reliability.

Before starting, it is important to point out that there are many options and techniques available to gain more insights and make ­better decisions on the performance of your assets and operation. It all comes down to knowing what the best fit is for your needs and what type of data you are using. Data comes in many shapes and sizes and can consist of time-series, labeled, random, intermittent, unstructured, and many more. All data holds information, it’s just a case of using the right approach to unlock it, and this is where the algorithms used within machine learning help decision-­makers.

Questions to Ask

It is important to understand the complexity involved with machine learning before you make a decision on what is appropriate for you and your organization. Here are some questions to ask yourself before implementing machine learning:

  • Question your data — What do you need to know, what are you looking for exactly? What do you want your data to tell you? What aren’t you seeing that you hope the data can provide?
  • Is your data clean? — Make sure your data is available, ready, and validated; the more data the better and the more accurate the outcomes will be.
  • Do you have enough data? — For accurate predictions, machine learning needs lots of historical data from which to train, then it can be applied to data in real time.
  • Which ML platform do I choose? — Choose your machine learning platform carefully considering interoperability.
  • Do I hire a data scientist, and how do they integrate? — With machine learning, there might be a need for a data scientist or analyst, but they shouldn’t be locked in a dark room.
  • Can I share the data output? — Knowledge gained through machine learning shouldn’t just be applied to one project at a time. Its scalability means it can and should be incorporated across the whole enterprise, delivering insights across every data-rich area. Plan to get the most out of machine learning.

Deeper Understanding

Machine learning makes complex processes and data easier to comprehend, and it is ideal for ­industries that are asset and ­data-rich. In any industry, the ­ability to recognize equipment ­failure, and avoid unplanned downtime, repair costs, and potential environmental damage, is critical to success. This is even more relevant in today’s turbulent times. With machine learning, there are numerous opportunities to improve the situation with predictive maintenance and the ability to ­predict critical failures ahead of time.

Predictive maintenance will be one of the most applicable areas where machine learning can be applied within the industrial sector. Predictive maintenance is the failure inspection strategy that uses data and models to predict when an asset or piece of equipment will fail so that proactive corrective actions can be planned in time.

Predictive Maintenance

Predictive maintenance can cover a large area of topics, from failure prediction, failure diagnosis, to recommending mitigation or maintenance actions after failure. The best maintenance is advanced forms of proactive condition-based maintenance. With the combination of machine learning and maintenance applications leveraging IIoT data, the range of positive outcomes and reductions in costs, downtime, and risk are worth the investment.

Whatever path is chosen, the benefits machine learning can offer are only now just starting to be revealed. Opportunity is rapidly developing with productivity advancements at the heart of the data-rich industry in which you work. While Healthcare, Financial, and the Automotive sectors are already advancing with machine learning, the industrial sector is quickly catching up.

Early adopters of machine learning are already reaping the benefits of predictive maintenance in the speed of information delivery, costs, and usefulness. This gives you more information and insight to make smarter decisions. Bentley Systems’ users are combining machine learning with Bentley’s other digitalization technologies to make this process even more beneficial — by making it model-centric and adding visualization dashboards, cloud-based IoT data, analytics, and reality modeling to machine learning, resulting in a complete solution for operations, maintenance, and engineering. Machine learning can also be leveraged within digital twins to provide even more predictive insights.

Having a predictive maintenance plan in place, powered by machine learning, will give you unprecedented insight into your operation and will lead to serious benefits in efficiency, safety, optimization, and decision-making.

The digital transformation for industry is now at a tipping point, with technologies all converging at the same time — a predictive maintenance approach to reliability and asset performance means that root cause analysis (RCA) could be a thing of the past. Machine learning takes into consideration the whole history of failures and identifies the signs of failure in advance.

* * The author is Senior Product Marketer for Asset Wise. Contact Bentley Germany: Phone +49-800-181-8565

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