Abstract
In the light of the significance of the rotating machinery and the possible severe losses resulted from its unexpected defects, it is vital and meaningful to exploit the effective and feasible diagnostic methods of its faults. Among them, the emphasis of the analysis approaches for fault type and severity is on the extraction of useful components in the fault features. On account of the common cyclostationarity of vibration signal under faulty states, fault diagnosis methods based on cyclostationary analysis play an essential role in the rotatory machine. Based on it, the fundamental definition and classification of cyclostationarity are introduced briefly. The mathematical principles of the essential cyclic spectral analysis are outlined. The significant applications of cyclostationary theory are highlighted in the fault diagnosis of the main rotating machinery, involving bearing, gear, and pump. Finally, the widely-used methods on the basis of cyclostationary theory are concluded, and the potential research directions are prospected.
Highlights
In practical engineering, owing to the changeable operation conditions of widely used rotating machinery, it is difficult to avoid the resulting faults of the components or system
In order to reduce the economic losses and security risks caused by the faults, it is of great significance to conduct the study of fault diagnosis methods [1,2,3,4]
Inspired by the application of cyclic spectral coherence (CSCoh) in signature processing, Chen et al employed it as a tool of feature extraction the intelligent fault diagnosis of bearings based on convolutional neural network learning of fault toinformation and obtain the distinguished features, which could make the (CNN) [77].feature
Summary
In practical engineering, owing to the changeable operation conditions of widely used rotating machinery, it is difficult to avoid the resulting faults of the components or system. The approaches of feature extraction involve stationary and non-stationary analysis based on the machinery vibration signal [10,11,12]. Analyzed the varying CS components of different rotating machinery including bearing and gearbox and discussed the relationship between angle domain and time cyclostationarity; a method was investigated combined blind deconvolution and demonstrated the validity and feasibility under the effects of the noise and speed [32,33,34,35]. With respect to the superiority of the CS analysis in non-stationary signal processing, this review plays an emphasis on the main and widely-used rotating machinery, involving bearing, gearing and pump. This research provides a novel perspective for the fault feature extraction of non-stationary signal and the exploration of the new diagnostic methods
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