February 2021

12 | Plant & Works Engineering www.pwemag.co.uk February 2021 Maintenance Matters Focus on: Maintenance 4.0 Y et, some engineering teams are sceptical about the extent to which predictive maintenance delivers measurable benefits. Organisations can find it difficult to determine a clear return on investment. Many are not confident they have the right data, or the right amount of failure data, for a functional algorithm to support predictive maintenance. Predictive maintenance is often incorrectly seen as a ‘black box’ solution that works as a closed application that receives operational data from machinery and uses an algorithm to predict when it will fail. In fact, the development of algorithms that can detect and predict equipment failings successfully needs to be fed and trained with domain knowledge too. So why doesn’t this always happen today and how can predictive maintenance change to overcome scepticism? Often those involved in predictive maintenance are data scientists with mathematical backgrounds or engineers who don’t possess data science skills. But the individuals that will be most successful with predictive maintenance will be those in companies that make an active effort to bridge the gap between data science and engineering. By having both sides working together, there is a great opportunity to generate high quality equipment failure data that can better train predictive maintenance algorithms. A key solution here is how software simulation tools can simplify the processes involved in predictive maintenance and bring both sides together. Such tools are designed to allow engineers less experienced in implementing predictive maintenance to carry out a variety of techniques to collect data and train algorithmic models. To do this, organisations need to understand what failure data looks like. This is a challenge because equipment doesn’t break easily or often, and it is expensive and inefficient to run equipment with the intention to break it for failure data collection. So, an important benefit of using these tools is how they can ensure much less real-world data is required to train algorithms properly. Software simulation models can be relied on to represent how physical apparatus functions in the field in the widest range of scenarios and conditions. Examples of companies that have used software in this way include: The cost of predictive maintenance scepticism Industrial equipment operators have increasingly turned to predictive maintenance to prevent catastrophic system failures as well as anticipate and schedule repairs correctly and minimise overall disruption to operations. Overall, predictive maintenance should generate significant cost savings and protect the company’s bottom line. Philipp Wallner* reports. Graphical interfaces like the diagnostic feature designer help engineers to identify the key parameters for their predictive maintenance algorithms. © MathWorks