June 2020

Focus on: Maintenance 4.0 Maintenance Matters The digital transformation of predictive maintenance, the so-called adoption of Maintenance 4.0 principles, builds on condition-based monitoring technologies but employs a far wider range of linked and networked sensors and devices as part of the Industrial Internet of Things. This feeds a far more comprehensive set of data points into new aggregation and analytics technologies, dramatically increasing the power and capability of predictive maintenance. In addition, advances in machine learning and artificial intelligence also have a role to play in predictive maintenance solutions. Data is collected from an ever-greater number of systems and devices. What is now important is how we harness that data to make meaningful decisions about the health of an asset. We can do this by utilising a combination of Edge and Cloud based solutions together with a set of analytical tools to analyse and interpret that data. In fact, in using an Edge based solution, we can also enable real-time decisions to be made on the operation and maintenance of assets around the plant. These are all advanced technologies, available now. Mitsubishi Electric for example has long offered its Smart Condition Monitoring (SCM) solution to alert impending problems on rotating machinery, facilitating a low cost predicitive maintenance solution. Beyond this, Mitsubishi Electric can provide Edge based solutions with embedded analytical tools, including the use of a digital twin. This can be used for predictive maintenance strategies amongst many other benefits in developing a greater understanding of the performance of a “real world” asset against its digital fully optimised model. In terms of the impact of artificial intelligence and machine learning, Mitsubishi Electric is embedding its proprietary AI technology into some of its product range moving forward. As an example a future servo range will have embedded predictive maintenance features for motor bearing failure detection and conveyor tension loss and their MELFA robot software provides predictive maintenance information on each individual axis of the robot. Enter the realm of prescriptive maintenance Asset maintenance strategies are still evolving as digital transformational technologies develop further and we now see the establishment of a maintenance regime that does not simply point to a need for future maintenance. It can also explicitly diagnose the problem and feed back in real time remedial action to extend the lifetime of the asset without significant impact to performance. This concept of using prescriptive analytics has resulted in a new maintenance paradigm, namely prescriptive maintenance. These new developing Edge technologies can not only be used to provide predictive maintenance information but could be extended to include links to logistics, inventory and MRO systems. By doing so, they provide a cohesive overview and execution of maintenance activity that has only been available in the past through the implementation of costly bespoke MES solutions. Thus, digital transformation is unlocking the potential of predictive maintenance, turning data into meaningful information that can help companies increase plant availability and asset utilisation. And there is more to come as we move into the realm of prescriptive maintenance, all helping companies to optimise production in an increasingly competitive age. Looking for affordable monitoring without CapEx approvals? dcosystems.co.uk +44 (0) 1285 359059 Our monthly subscription fee provides the hardware and software you need to get started including installation training and support