Abstract
The fault vibration signal of a bearing has nonstationary and nonlinear characteristics and can be regarded as the combination of multiple amplitude- and frequency-modulation components. The envelope of a single component contains the fault characteristics of a bearing. Local characteristic-scale decomposition (LCD) can decompose the vibration signal into a series of multiple intrinsic scale components. Some components can clearly reflect the running state of a bearing, and fault diagnosis is conducted according to the envelope spectrum. However, the conventional LCD takes a single-channel signal as the research object, which cannot fully reflect the characteristic information of the rotor, and the analysis results based on different channel signals of the same section will be inconsistent. To solve this problem, based on full vector spectrum technology, the homologous dual-channel information is fused. A vector LCD method based on cross-correlation coefficient component selection is given, and a simulation analysis is completed. The effectiveness of the proposed method is verified by simulated signals and experimental signals of a bearing, which provides a method for bearing feature extraction and fault diagnosis.
Highlights
Rotating machinery is developing in the direction of high speeds, heavy loads, and high reliability, which places higher requirements on mechanical transmission equipment [1].e operational state of mechanical equipment is changing, and its safe, stable, and reliable operation must be ensured
The vector Local characteristic-scale decomposition (LCD) has a good analysis result when applied to fault signals with frequency or amplitude modulation, which can enhance the accuracy of fault diagnosis. e adoption of the cross-correlation coefficient avoids repeated analysis between multiple components and simplifies the analysis, for a unique and accurate conclusion
Is bearing experimental application shows that the proposed method can be applied to rolling bearing fault diagnosis. e screening of the optimal intrinsic scale components (ISCs) can simplify fault diagnosis and clearly display the typical features. e full vector fusion between optimal ISCs of the x- and y-signals gives an accurate and unique conclusion for fault diagnosis. e vector LCD provides an easy way to extract fault features
Summary
Rotating machinery is developing in the direction of high speeds, heavy loads, and high reliability, which places higher requirements on mechanical transmission equipment [1]. E key to fault diagnosis of rolling bearings is to extract effective feature information from vibration signals containing complex frequencies [9]. Zheng [19] proposed local characteristicscale decomposition (LCD), a nonstationary signal analysis method that adaptively decomposes a signal to a series of intrinsic scale components in different scales. (1) A signal processing method, vector LCD, is proposed, which fully considers homologous signals and intrinsic scale components (ISCs).
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