Abstract
As the main cause of failure and damage to rotating machinery, rolling bearing failure can result in huge economic losses. As the rolling bearing vibration signal is nonlinear and has non-stationary characteristics, the health status information distributed in the rolling bearing vibration signal is complex. Using common time-domain or frequency-domain approaches cannot easily enable an accurate assessment of rolling bearing health. In this paper, a novel rolling bearing fault diagnostic method based on multi-dimensional characteristics was developed to meet the requirements for accurate diagnosis of different fault types and severities with real-time computational performance. First, a multi-dimensional feature extraction algorithm based on entropy characteristics, Holder coefficient characteristics and improved generalized fractal box-counting dimension characteristics was performed to extract the health status feature vectors from the bearing vibration signals. Second, a grey relation algorithm was employed to achieve bearing fault pattern recognition intelligently using the extracted multi-dimensional feature vector. This experimental study has illustrated that the proposed method can effectively recognize different fault types and severities after integration of the improved fractal box-counting dimension into the multi-dimensional characteristics, in comparison with existing pattern recognition methods.
Highlights
Rolling bearings are widely used in almost all types of rotating machinery [1]
A novel rolling bearing fault diagnostic method was developed to meet the requirements for accurate diagnosis of different fault types and different severities with real-time computational performance
After the dominant fault feature vectors {E1, E2, H1, H2, D1, D2, D3, . . . , DK} were extracted from the rolling element bearing vibration signals with different fault types and severities through the multidimensional feature extraction algorithm based on the entropy characteristics, the Holder coefficient characteristics and improved generalized fractal box-counting dimension characteristics, the sample knowledge base for the grey relation algorithm (GRA) was established based on the fault symptoms and the fault pattern
Summary
Rolling bearings are widely used in almost all types of rotating machinery [1]. Rolling bearing failure is one of the main causes of failure and damage to rotating machinery, and can result in huge economic losses [2,3,4]. Some entropybased methods (such as hierarchical entropy [17], fuzzy entropy [18], sample entropy [19], approximate entropy [20,21], hierarchical fuzzy entropy) have been proposed for extracting the dominant eigenvectors that characterize fault features from bearing vibration signals and have achieved some effect. In order to ensure diagnostic accuracy, some optimization algorithms [28] are often used to improve the effectiveness of SVMs. With the aim of solving the problem that traditional time-domain and frequency-domain methods cannot make an accurate diagnosis of rolling bearings, a rolling bearing online fault diagnostic method is proposed based on multi-dimensional feature extraction theory and grey relation pattern recognition theory.
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