With the rise of Industry 4.0 and the implementation of “smart machines”, it has opened new opportunities to optimise the utilisation of all data gathered from the machines. The most prevalent model found in predictive maintenance is data-driven and based on statistical process control, pattern recognition or machine learning algorithms. A big obstacle faced by maintenance staff is understanding the data retrieved from the machines, as the volume is often overwhelming and needs to be processed to be fully useful. Hence, this paper identifies the requirements for a successful implementation of predictive maintenance.
The Requirements for Predictive Maintenance Strategy and the Data Required for Successful Implementation
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