Two Stage Data Fusion of Acoustic, Electric and Vibration Signals for Diagnosing Faults in Induction Motors
The increasing demand for predictive maintenance is a main driver of the development of better fault diagnosis algorithms. Each condition monitoring approach has its own strengths and weaknesses; there is not a single technique that can diagnose all types of faults. As a result, it can be a challenge to find the root cause of a problem when only a single feature is monitored. There is also a greater risk of missed- or false-alarms. It has been shown that data fusion, combining multiple features, can improve the effectiveness of fault diagnosis. In recent work, a two-stage Bayesian inference approach, in which data is fused at both a local, or component, level, as well as at a global, or system-wide level has been shown to refine the diagnostic assessment of machinery comprised of a number of interacting components. In this paper, we show that the approach may also be applied to combine information from multiple, diverse condition monitoring systems. Acoustic, electric and vibration signals were measured from healthy and faulty induction motors, operating under normal and noisy working conditions. The proposed method was shown to increase the reliability of the health assessment of the induction motors, reducing the risk of missed and false alarms.
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