Factory-wide predictive maintenance in heterogeneous environments

Predictive maintenance is an important strategy to increase overall productivity in a technology-pervaded domain like manufacturing. However, existing approaches cannot be applied in a straight-forward manner because they insufficiently support the multitude of diverse machines on the shop floor and provide only limited automation for failure prediction. In this paper, we identify seamless integration, heterogeneity, and flexibility as the main challenges when applying predictive maintenance in a factory-wide setting. We propose an architecture for a predictive maintenance framework. We implemented the framework and built a prototype that estimates the probability of breakdowns based on power consumption.