The future of machine diagnostics in smart manufacturing environments: design of an intelligent fault simulator apparatus

Abstract

Intelligent fault diagnosis requires an understanding of the dynamic behavior of mechanical components, especially within rotating machinery, as well as the ability to accurately generate controlled data. This study investigates the modeling, generation, acquisition, and analysis of vibration signals in ball bearings. By designing and manufacturing a vibration analysis test rig, it is now possible to test the ability of predictive models to diagnose vibration signatures. With this data, machine learning architecture can accurately classify faults and estimate the remaining useful life of a component.