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Vibration-based approach to lifetime prediction of electric motors for reuse

机译：基于振动的电动机寿命预测方法，可重复使用

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This paper is concerned with lifetime prediction of components in washing machines. Vibration signals were measured on electric motors during an accelerated lifetime test ranging from 26.7 to 38.5 simulated years. Loose bearings have initiated air-gap eccentricity and rotor-to-stator rubbing, which resulted in a motor breakdown. Significant frequency bands were identified using a spectral comparison based on the constant percentage bandwidth (CPB) spectrum. Increasing trends were extracted from several vibration indicators, such as envelope cepstrum (EC) and a weighted integral of CPB differences. The EC is computed as the real cepstrum of the envelope signal obtained by demodulating the band identified by the CPB comparison. Hence the EC is more sensitive as it employs a priori information provided by historical data. The fault was first detected 9.7 years in advance and confirmed 5.3 years before the breakdown. The indicators can be integrated with a recent methodology based on Weibull analysis and neural network modelling.
机译：本文涉及洗衣机组件寿命的预测。在加速寿命测试中，在26.7至38.5个模拟年的加速寿命测试期间，在电动机上测量了振动信号。松动的轴承已引起气隙偏心和转子至定子的摩擦，从而导致电动机故障。使用基于恒定百分比带宽（CPB）频谱的频谱比较来识别重要的频段。从几个振动指标（例如包络倒谱（EC）和CPB差异的加权积分）中提取了增加的趋势。 EC被计算为通过解调CPB比较所标识的频带而获得的包络信号的真实倒谱。因此，EC会采用历史数据提供的先验信息，因此更加敏感。该故障最早在9.7年之前被发现，并在故障发生前5.3年被确认。这些指标可以与基于Weibull分析和神经网络建模的最新方法集成。

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