This paper presents two approaches to power circuit breakers condition assessment. The first one covers a wide variety of machine learning classification algorithms where the input for is manually selected feature set. second utilizes deep based on convolutional neural network. Both revolve around idea behind spectral kurtosis, which exploits its visual representation in form <italic xmlns:mml=...

Transient impulses caused by local faults are critical informative indicators for rolling element bearing fault diagnosis. The methods accurately extracting transient while suppressing strong background noise and interference components have received extensive studies. In this article, a novel diagnosis scheme based on optimized wavelet packet denoising modulation signal bispectrum is proposed,...

The rotating machinery is a common class of machinery in the industry. The root cause of faults in the rotating machinery is often faulty rolling element bearings. This paper presents a novel technique using artificial neural network learning for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (harmmean and median), whic...

Developments in data storage and sensor technologies have allowed the cumulation of a large volume from industrial systems. Both structural non-structural systems are collected, which covers formats time-series, text, images, sound, etc. Several researchers discussed above were mostly qualitative, ceratin techniques need expert guidance to conclude on condition gearboxes. But, this study, an im...

This paper brings up a novel method for detecting induction motor stator winding faults at an early stage. The contribution of the work comes from the delicate handling of motor vibration by applying envelope analysis, which makes it possible to capture electrical short-circuit signature in mechanical signals, even if the magnitude of the fault is fairly incipient. Conventional induction motor ...

Optimal demodulation band extraction is a significant step in rolling bearing fault analysis. However, existing methods, primarily based on global indexes and neglecting negative local outliers, cannot identify compound faults intense noise environments. To address this problem, novel method weighted geometric cyclic relative entropy (WGCRE) proposed. WGCRE defined the sub-bands model of logari...

Abstract Background The acoustic emission (AE) analysis has been used increasingly for gearbox diagnostics. Since AE signals are of non-linear, non-stationary and broadband nature, traditional signal processing techniques such as envelope spectrum must be carefully applied to avoid a wrong fault diagnosis. One technique that enhance the demodulation process vibration is empirical mode decomposi...