Abstract
Fault diagnosis of critical rotating machinery components is necessary to ensure safe operation. However, the commonly used rotating machinery fault diagnosis methods are generally based on the single-channel signal processing method, which is not suitable for processing multi-channel signals. Thus, to extract features and carry out the intelligent diagnosis of multi-channel signals, a novel method for rotating machinery fault diagnosis is proposed. Firstly, a novel nonlinear dynamics technique named the multivariate generalized refined composite multi-scale sample entropy was presented and applied to extract fusion entropy features of multi-channel signals. Secondly, a practical manifold learning known as supervised isometric mapping was introduced to map the high-dimensional fusion entropy features in a low-dimensional space. In a third step, the Harris hawks optimization-based support vector machine was applied to carry out the intelligent fault recognition. Finally, aiming to verify the effectiveness of the proposed method and present its advantages, it was applied to analyze the rotating machinery system bearing and gear data. The experimental results have shown that the method at hand can accurately identify various faults in both the bearings and gears. Furthermore, in addition to being suitable for multi-channel signal fault diagnosis, it had higher recognition accuracy compared to other multi-channel or single-channel methods.
Highlights
Rotating machinery has been widely employed in various industrial fields, including petroleum, aerospace, metallurgy, energy, and chemistry
During the previous two decades, intelligent fault diagnosis methods based on vibration signals were gradually developed and subsequently applied in rotating machinery
Used intelligent fault diagnosis methods are often based on single-channel signals [6,7,8,9,10]
Summary
Rotating machinery has been widely employed in various industrial fields, including petroleum, aerospace, metallurgy, energy, and chemistry. The above-mentioned sample entropy-based algorithms performed very well in rotating machinery fault diagnosis; all the algorithms belong to the group of single-channel analysis methods. The MMSE and MGMSE algorithms are introduced Based on these two algorithms the MGRCMSE is proposed after implementing the multi-variate generalized composite coarse-grained structure and refined operation. Based on MGMSE, in this paper, we propose the MGRCMSE algorithm, which was developed combining the multi-variate composite coarse-grained structure and refined entropy operation. The entropy curves obtained by the MGRCMSE (or MRCMSE) for each group of signals have displayed lower fluctuation compared with the MGMSE (or MMSE) This is since MGRCMSE (or MRCMSE) adopts the multi-variate refined composite coarse-grained structure, allowing it to comprehensively consider multiple sequences at s. Significant improvements were identified when using MGRCMSE when measuring the complexity of multi-channel signals
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