Abstract
Rotary machines are widely used in various applications. A reliable machinery fault detection technique is critically needed in industries to prevent the machinery system’s performance degradation, malfunction, or even catastrophic failures. The challenge for reliable fault diagnosis is related to the analysis of non-stationary features. In this paper, a wavelet spectrum (WS) technique is proposed to tackle the challenge of feature extraction from these non-stationary signatures; this work will focus on fault detection in rolling element bearings. The vibration signatures are first analyzed by a wavelet transform to demodulate representative features; the periodic features are then enhanced by cross-correlating the resulting wavelet coefficient functions over several contributive neighboring wavelet bands. The effectiveness of the proposed technique is examined by experimental tests corresponding to different bearing conditions. Test results show that the developed WS technique is an effective signal processing approach for non-stationary feature extraction and analysis, and it can be applied effectively for bearing fault detection.
Highlights
Rolling element bearings are widely used in rotary machinery
Feature extraction is a process in which health condition related features are extracted by appropriate signal processing techniques, whereas fault diagnosis is a decision-making process to estimate bearing health conditions based on the extracted representative features
Feature extraction plays the key role for bearing health condition monitoring, whereas non-robust features may lead to false alarms or missed alarms in diagnostic operations [3]
Summary
Rolling element bearings are widely used in rotary machinery. A reliable bearing fault diagnostic technique is critically needed in a wide array of industries to prevent machinery performance degradation, malfunction, or even catastrophic failures [1]. It is seen that some periodic features are prominent (e.g., in Figures 2(b), (e), and (f)) whereas others are less pronounced (e.g., in Figures 2(a), (c), and (d)) Another key process in bearing incipient fault detection is how to properly choose the more contributive wavelet bands to integrate cross-correlation coefficient functions to highlight the periodic features.
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