February 2021

Focus on: Maintenance 4.0 Maintenance Matters February 2021 www.pwemag.co.uk Plant & Works Engineering | 13 Safran – the high-tech industrial group used simulation models to train a neural network used to actively monitor and predict anomalies in a hydraulic press. The company leveraged simulation models to create data representing faulty machinery allowing the team to combat the problem posed by a lack of real-world failure data. Mondi – the packaging and paper goods manufacturer developed a predictive maintenance application with software to identify potential equipment issues. The system was running within a matter of months even though the company lacked data scientists with expertise in machine learning Baker Hughes – the oil field service organisation applied software to develop a pump health monitoring solution using data analytics for predictive maintenance. The result was that the company reduced equipment downtime costs by up to 40 percent while also decreasing the number of onsite trucks. All of these cases show there is an opportunity to get data science and engineering teams collaborating to produce enough failure data to train predictive maintenance algorithms sufficiently in a cost-effective way. Software simulation tools simplify the process, make predictive maintenance algorithms more powerful and reduce the amount of data overall needed for them to be properly trained. Future looks bright Predictive maintenance is set to greatly evolve through the rest of the decade. At present, most predictive maintenance algorithms are close to equipment onsite like an edge server that collects data locally in a production facility or wind farm. Over the next three to five years, the calculation power of industrial controllers and edge computing, and use of cloud systems, will rapidly increase. This lays the foundation for better software functionality on production systems. Predictive maintenance will consider data not just from one machine or site but from multiple factories and across equipment from different vendors. Depending on the requirements, these AI- based algorithms will be deployed on non-real-time platforms in addition to real-time systems such as programmable logic controllers. Cloud computing will become ever more relevant with predictive maintenance systems feeding data from equipment from anywhere in the world into a cloud platform. Cloud computing will allow manufacturers to collect data from numerous sources to train predictive maintenance algorithms more efficiently. Despite some scepticism over data ownership and security, it won’t be long until cloud-based predictive maintenance becomes a reality. For engineers that apply predictive maintenance to their production lines, there are a myriad of benefits that can be realised. Engineering teams that remain sceptical and haven’t determined how predictive maintenance can be monetised risk putting their organisations at a competitive disadvantage. Fortunately, there are tools available to simplify processes and combine domain expertise and machine learning, enabling predictive maintenance and its benefits to be feasible for any organisation considering it. * Philipp Wallner* is industry manager at MathWorks LEFT: Mondi developed health monitoring and predictive maintenance applications that identify potential equipment issues and help reduce downtime and maintenance costs. © MathWorks