Performance of Envelope Demodulation for Bearing Damage Detection on CWRU Accelerometric Data: Kurtogram and Traditional Indicators vs. Targeted a Posteriori Band Indicators

Abstract

Envelope demodulation of vibration signals is surely one of the most successful methods of analysis for highlighting diagnostic information of rolling element bearings incipient faults. From a mathematical perspective, the selection of a proper demodulation band can be regarded as an optimization problem involving a utility function to assess the demodulation performance in a particular band and a scheme to move within the search space of all the possible frequency bands {f, Δf} (center frequency and band size) towards the optimal one. In most of cases, kurtosis-based indices are used to select the proper demodulation band. Nevertheless, to overcome the lack of robustness to non-Gaussian noise, different utility functions can be found in the literature. One of these is the kurtosis of the unbiased autocorrelation of the squared envelope of the filtered signal found in the autogram. These heuristics are usually sufficient to highlight the defect spectral lines in the demodulated signal spectrum (i.e., usually the squared envelope spectrum (SES)), enabling bearings diagnostics. Nevertheless, it is not always the case. In this work, then, posteriori band indicators based on SES defect spectral lines are proposed to assess the general envelope demodulation performance and the goodness of traditional indicators. The Case Western Reserve University bearing dataset is used as a test case.

A New Method for Bearing Fault Diagnosis across Machines Based on Envelope Spectrum and Conditional Metric Learning

“…In the contemporary landscape of fault diagnosis, an array of algorithms have been developed based on the principles of mechanical fault theory. These methods encompass diverse techniques, such as resonance demodulation [7], envelope demodulation [8,9], generalized demodulation [10], and order ratio analysis [11]. The recent surge in the field of bearing fault diagnosis can be attributed to the continuous advancements in deep-learning technologies.…”

Cross-domain bearing fault diagnosis method based on SMOTENC and deep transfer learning under imbalanced data

“…Currently, most methods for bearing fault diagnosis are based on vibration signals. Vibration signals contain periodic fault impulse impacts, and based on this characteristic, traditional fault diagnosis methods such as resonance demodulation [7], envelope demodulation [8,9], generalized demodulation [10], and order ratio analysis [11] have been proposed and achieved satisfactory results. With the development of artificial intelligence, fault diagnosis methods based on deep learning have emerged, including convolutional neural networks [12], autoencoders [13], recurrent neural networks [14], generative adversarial networks [15], and graph neural networks [16].…”