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This article presents a methodology that provides a continuous assessment of predictive maintenance (PdM) technologies with respect to specific business scenarios. The methodology integrates existing reliability and maintenance business analysis techniques and standards. The positive impacts that may have implementing these technologies have always been in mind. A critical simulation step is also added where different predictive maintenance strategies are simulated in order to obtain the optimal maintenance strategy. This Monte Carlo simulation relies on the reliability information based on the probability density distribution of failure for the system or component, providing as a result the optimal strategy among the proposed options. The article finally explains how this methodology has a positive impact not only on the cost-effectiveness of maintenance processes, but also on the maintenance information available.

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A new approach for the control of the life cycle of rolling bearings in machine tools is presented. The approach is based on a new simulation tool that has been developed using the well-known ISO 281 standard as a reference. This new tool improves the accuracy of the estimations in two fields: model-based and feature-based prognostics. The tool provides accurate end-of-life estimations, thanks to the determination of more realistic component loads for a defined manufacturing cycle. In addition, it can be embedded in monitoring devices using real machine tool usage data and calculate remaining useful life (RUL) of the analyzed component. Experimental tests confirm that the ISO 281 standard overestimates end-of-life of bearings under controlled conditions and presents vibration analysis as a key tool for an early detection of bearing failure. Indeed, the application of the module for remaining useful life calculation should be combined with vibration-based condition monitoring in order to detect unexpected component degradation.