Predicting Reliability: How Machine Learning Creates Added Value

Predictive Maintenance and Machine Learning Predicting Reliability: How Machine Learning Creates Added Value

2020-01-24 Richard Irwin*

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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.

Viele Begriffe, und alle zielen auf Methoden aus Daten zu lernen und Vorhersagen treffen zu können.(Source: ©iuriimotov; aleksander1 - stock.adobe.com) — Viele Begriffe, und alle zielen auf Methoden aus Daten zu lernen und Vorhersagen treffen zu können.
(Source: ©iuriimotov; aleksander1 - stock.adobe.com)

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.

Open Minds Think OpenPlant: How to realize the benefits of increased interoperability, reducing costs, increasing collaboration and improving design capabilities for the plant lifecycle.

The 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.

With its digital twin services Bentley follows the trend to digitization. (©nirutft - stock.adobe.com)

Going Digital: Basic 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.

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Developing a 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.

The 10 Chemical Innovations with the Highest Sustainability Potential

Since the establishment of the Periodic Table of Elements (PSE) 150 years ago, not only the number of known elements has increased, but new technologies have also emerged: Chemical pesticides to protect the harvest, batteries for energy on the move and plastics in every form, color, and function. The current focus is on the sustainability aspect. But here, too, chemistry offers new solutions. The 10 most promising technologies for a more sustainable world have been selected by the chemical association IUPAC. Originalpublication: Fernando Gomollón-Bel: Ten Chemical Innovations That Will Change Our World: IUPAC identifies emerging technologies in Chemistry with potential to make our planet more sustainable, Chemistry International. Band 41, Heft 2, Online erschienen: 01.04.2019; DOI: 10.1515/ci-2019-0203(Picture: Pixabay/markmags)

Nanopesticides – Small Plant Protection Products with a big Effect Problem: It's getting fuller in the world. Some forecasts predict a world population of up to ten billion people in 2050. Even today, the supply of basic foodstuffs is a problem in some places. The pesticides inevitably used to protect harvests from pests not only have advantages. Because they often hit useful insects and also harm the environment and, ultimately, humans themselves. Goal: To get more sustainable agriculture requires the use of environmentally friendly technologies. The aim is to keep the impact on the environment to a minimum, consume less water and limit contamination by chemical insecticides.Solution: Tailor-made nano-systems offer opportunities to bring pesticides to their destination in the smallest possible dosage and with the greatest possible precision. This could minimize the negative impact on humans and the environment. At present, research is still underway into such solutions and the opportunities and risks are being assessed. Under laboratory conditions, researchers have already achieved improvements of an order of magnitude, but field trials have yet to produce such results. Although nanotechnology is not the only building block for sustainable agriculture, it can make a significant contribution to more efficient crop protection.(Picture: Pixabay/Bru-nO)

Enantioselective Organocatalysis – Easy Catalysis without Heavy Metals Problem: In chemistry, high-performance catalysts are often based on precious metals and are therefore very expensive. This represents an additional hurdle, especially for research, because projects with precious metal catalysts cause additional costs and therefore cannot always be financed. Goal: Efficient and environmentally friendly catalysis is required, which can also be implemented at low cost. Solution: Just as much of chemistry is inspired by nature, catalysis can of course be improved. The idea is metal-free catalysts. This has been the subject of research since the late 1990s. Previous successes have often been diminished by the high catalyst loading required. The reactivation of the spent catalyst was also usually difficult. In the meantime, however, researchers have already developed better organic catalysts that have largely solved these problems. Although they lag further behind metal catalysts in terms of efficiency, they are much cheaper - precisely because they do not require precious metals. Young scientists can thus set up their own research projects on catalysis without the need for expensive precious metals or laboratory equipment such as glove boxes for air-sensitive metal catalysts. Today, there are already established processes in the industry that synthesize active substances and chemicals using asymmetric organocatalysis.(Picture: Pixabay/erik_stein)

Solid Batteries – Polymer and Glass Rechargeable Batteries Problem: Powerful power storage systems are needed to drive the energy revolution forward. This is the only way to efficiently implement electric mobility and decentralized power supply. Lithium-ion batteries are currently the standard. But they are heavy and run the risk of leaking environmentally harmful and combustible electrolyte fluid in the event of an accident. Goal: Back in the 19th century, Michael Faraday proposed a concept for a battery that consisted entirely of solid matter and no longer needed a liquid electrolyte. Solution: Today, the first advances have already been made in the field of solid batteries. For example, models with glass electrolyte were presented by John Goodenough, one of the co-developers of the lithium-ion batteries widely used today. Polymer-based batteries have also made it into production. The new battery systems are still very expensive and there is currently no economic competition to the established solutions. In the long term, however, they already score points because they are lighter than lithium-ion batteries, have a higher storage density and perform well even at high temperatures. However, several years of research will still be required for most applications.(Picture: Pixabay/rawpixel)

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Predictive Maintenance and Machine Learning

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.

Brian Robins, Vice President of Product and Industry Marketing at Bentley (Bentley)

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.

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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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