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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]