Remaining Useful Life Prognostics for the Rolling Bearing Based on a Hybrid Data-Driven Method

Wang, Yingang; Guo, Runxia; Liu, Guihang

doi:10.1109/irce50905.2020.9199243

Cited by 1 publication

(1 citation statement)

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“…Given the impact and the high costs usually associated with rotor failures, CMD, health assessment, and data-driven prognostic strategies that can refine the RUL prediction of IMs have been investigated [20][21][22]. Data-driven approaches are not the only way to do this; there exist two other approaches, namely model-based and hybrid approaches [23][24][25][26]. The model-based approaches use mathematical models to represent rotor behavior and degradation phenomena.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model

2021

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Rotating machines such as induction motors are crucial parts of most industrial systems. The prognostic health management of induction motor rotors plays an essential role in increasing electrical machine reliability and safety, especially in critical industrial sectors. This paper presents a new approach for rotating machine fault prognosis under broken rotor bar failure, which involves the modeling of the failure mechanism, the health indicator construction, and the remaining useful life prediction. This approach combines signal processing techniques, inherent metrics, and principal component analysis to monitor the induction motor. Time- and frequency-domains features allowing for tracking the degradation trend of motor critical components that are extracted from torque, stator current, and speed signals. The most meaningful features are selected using inherent metrics, while two health indicators representing the degradation process of the broken rotor bar are constructed by applying the principal component analysis. The estimation of the remaining useful life is then obtained using the degradation model. The performance of the prediction results is evaluated using several criteria of prediction accuracy. A set of synthetic data collected from a degraded Simulink model of the rotor through simulations is used to validate the proposed approach. Experimental results show that using the developed prognostic methodology is a powerful strategy to improve the prognostic of induction motor degradation.

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