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, optimisation, and decision making. Article by Richard Irwin, Bentley Systems.
One of the goals of reliability is to identify and manage the risks around assets that could fail, causing unnecessary and expensive downtime. We know it is important to identify areas of potential failures and rate them in terms of likelihood and consequence. We have put good reliability strategies in place and have implemented proactive condition-based maintenance programs. Today, machine learning is helping maintenance organizations get to an elevated level of situational intelligence to guide actions and provide early warnings of impending asset failure that previously remained undetected. Machine learning is paving the way for smarter and faster ways to make data-driven decisions in predictive maintenance.
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. While machine learning has typically been linked with industries such as transportation and banking (think 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 insight 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, labelled, 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.
6 Questions to Answer Before Investing in Machine Learning
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 by 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 insight into any area rich in data. Plan to get the most out of machine learning.
The Route to 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, and repair costs, among others, 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 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 Industrial IoT (IIoT) data, the range of positive outcomes and reductions in costs, downtime, and risk are worth the investment.
Whatever path is chosen, the benefits of machine learning can offer to big data are only just being brought to fruition. Opportunity is rapidly developing with productivity advancements at the heart of the data-rich industry in which you work. While healthcare, financial, automotive, oil and gas, electric and power, and water utility sectors are already advancing with machine learning, there’s another sector leading the way in this fast-moving digital transformation: manufacturing.
Manufacturing has always been the main industry when mentioned alongside machine learning, and for good reason, as the benefits are very real. These benefits include reductions in operating costs, improved reliability, and increased productivity—three goals that relate to the holy trinity of manufacturing. To achieve this, manufacturing also requires a digital platform to capture, store, and analyse data generated by control systems and sensors on equipment connected via the IoT.
Preventative maintenance is key in improving uptime and productivity, so greater predictive accuracy of an equipment failure is essential with increased demand. Furthermore, by knowing what is about to fail ahead of time, spare parts and inventory can use the data to ensure they align with the prediction. Improving production processes through a robust condition monitoring system can give unprecedented insight into overall equipment effectiveness by monitoring air and oil pressures and temperatures regularly and consistently.
Early Case Study Example: Process Manufacturing and Condition Monitoring
This example is centred around a steel manufacturer who routinely shuts down operations to perform maintenance on its assets, which is very costly. The steel output can sometimes warp or “crimp” during the production process as it travels through different stages. These failures can only be corrected every six months (as well as monthly for smaller fixes) during planned—and very expensive—maintenance that involves long periods of downtime. The main goals of applying machine learning here were to: reduce defects and locate root cause; identify key variables that matter the most; and prioritize assets during a shutdown.
The first part of the machine learning process was to sort the data into a self-organizing map using neural networks to organize data into 10 distinct classes based on parameters of the steel, such as thickness and weight, as they entered each manufacturing stage. Other techniques included decision trees to learn the pattern of data and to identify which features were important in those patterns; asset health prioritization to provide ranking; asset health indexing to determine the health of the assets; principle component analysis to reduce the dimensionality of the data; and clustering/anomaly detection, which highlights how each stand deviates from its normal operating mode.
What developed was a method for dealing with different types of products, the ability to identify the top variables associated with production defects, and a process for applying anomaly detection to equipment in an industrial plant. It was shown that these processes could reduce the need for extensive analysis of equipment and give operators better tools and more insights to make maintenance decisions. A significant amount of time is spent locating the cause of the issues and performing maintenance. The new algorithm can be run before planning the shutdown, and it can identify which stand to prioritize during shutdowns through analysis of the asset anomaly charts. Focusing on assets that are the most at risk optimizes the shutdown, as it is only conducted for a limited time.
Digitalisation and Transformation with Machine Learning
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 digitalisation 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, the result is 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, optimisation, and decision making. The digital transformation for the 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.
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