Many industries are making efforts to minimize the losses caused by shutdown of manufacturing facilities and to set an optimal maintenance schedule. In this context, prognostics, which predict remaining useful life (RUL) based on information extracted from sensory signals, have attracted
attention. There are three methods to perform life prediction: physics-based, data-based, and hybrid. However, data-driven methods are the only way to apply them to a complex industrial facility. By assuming multiple degradation unit data, we can extract various features from the data and select the best feature to create a health index(HI). In this study, we propose a new method for the feature selection step that greatly determines the performance of RUL prediction. Proposed algorithm can automatically select features that are monotonic and have a consistent level of value in normal and failure zone. We validate our method using real degradation data acquired from bearing life testbed.
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