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Gearbox is a key component in mechanical transmission and faults on gears will lead to breakdowns and unscheduled downtime. Health condition monitoring and remaining useful life (RUL) prediction can provide sufficient leading time for gearbox timely maintenance. To some degree, the RUL prediction accuracy relies on the performance of the diagnostic features on reflecting the degradation of gears during its lifetime. However, most current commonly used features fail to reveal the fault mechanism hidden behind vibration signal and hold poor capability on noise cancellation. In this paper, modulation signal bispectrum (MSB) is proposed to reveal the signal modulation mechanism and monitor the health condition of gears. Then, an improved relevance vector machine (IMRVM) is introduced to realize the process of RUL prediction. Last, a run-to-failure test rig is designed to verify the effectiveness the MSB features for RUL prediction. Results show that MSB possesses better performance on denoising and capturing the weak variation due to modulation in gear system. The optimal MSB features after selection show better performance on reflecting the degradation of the gear and have higher prediction accuracy for gear RUL prediction comparing with RMS, kurtosis and so on. These findings provided more useful and practical information for gear RUL prediction and gearbox maintenance. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into sideband vibro-acoustics suppression of the permanent magnet synchronous motor (PMSM), associated with several periodic signal-based dithering modulations. Mathematical derivations of four typical periodic signal-based schemes are deduced with the power spectral density (PSD) method. The individual modulation parameters, such as bandwidth and modulation ratio, are determined the values to minimize the sideband vibro-acoustics. The frequency distributions of the sideband components are discussed by the analytical derivations. The relationship between the electromagnetic components and mechanical responses is reconstructed by coupling with temporal features. For verification, the experimental tests with a prototype PMSM are introduced under different operational conditions. According to the results and figures, both magnitude suppression and frequency distribution are accurately correlated with sideband harmonic components as well as vibro-acoustic responses. This investigation thus clearly holds promise for the evaluation and optimization in powertrain used by electric vehicles. [ABSTRACT FROM AUTHOR]

In the motor current signal, the characteristic frequency of broken rotor bar (BRB) fault is modulated by the supply frequency and it decreases with the decrease of the load, resulting it to be easily buried under light load conditions. Teager–Kaiser energy operator (TKEO) has shown better performance to detect the BRB faults than classical methods, such as envelope analysis and spectral analysis. However, the original definition of TKEO leads to its result lack of physical meanings and the causal processing in TKEO can lead to phase distortion and nonideal filter characteristics. Therefore, this article proposes a normalized frequency-domain energy operator (FDEO) for the BRB fault diagnosis, which does not require causal processing and calculates multiple differentiations in the frequency domain with equal accuracy in one operation. Furthermore, the normalized FDEO removes the influence of the supply frequency followed by the spectral analysis to extract fault features. The mathematical model of induction motor (IM) under healthy and faulty condition is studied in this article. Then, the proposed approach is experimentally validated with seeded one and two BRB faults operating under various load conditions. To verify the effectiveness, the results are compared with the TKEO, envelope analysis, and spectral analysis. It was found that the proposed method provides slightly obvious fault features with respect to the TKEO, especially when the IMs run under light load conditions with two BRB faults. [ABSTRACT FROM AUTHOR]