Bearings are widely used in industries and construction for shaft supporting or seismic isolation. In recent years, their fault diagnosis, especially under variable, or fluctuant conditions, has received increasing attention. Sufficient monitoring data are usually required for bearing diagnosis. However, sufficient data cannot be guaranteed in some engineering cases with limitations of the transmission channel bandwidth or onboard/onsite computational capabilities. Fortunately, the emerging compressed sensing technique, which provides an effective solution to data compression and processing, has the ability to transform traditional monitoring data to the compressed information domain for a highly effective diagnosis under fluctuant conditions. This study proposes a bearing fault diagnosis method under fluctuant conditions based on compressed sensing theory. First, a random matrix is constructed as the measurement matrix and is employed to compress the original signal into the compressed information domain. Then, reconstruction-evaluation based fault diagnosis method is conducted with compressed signals. Moreover, the compressed signals used for fault diagnosis are reconstructed on the remote side. The experimental results provide evidence that the proposed method can effectively reduce the data volume required for bearing diagnosis and maintain an accuracy similar to current approaches, and the reconstructed signals can be used for other fault diagnosis methods.
Purpose
The purpose of this paper is to propose a fault diagnosis method for rolling bearings, in which the fault feature extraction is realized in a two-dimensional domain using scale invariant feature transform (SIFT) algorithm. This method is different from those methods extracting fault feature directly from the traditional one-dimensional domain.
Design/methodology/approach
The vibration signal of rolling bearings is first transformed into a two-dimensional image. Then, the SIFT algorithm is applied to the image to extract the scale invariant feature vector which is highly distinctive and insensitive to noises and working condition variation. As the extracted feature vector is high-dimensional, kernel principal component analysis (KPCA) algorithm is utilized to reduce the dimension of the feature vector, and singular value decomposition technique is used to extract the singular values of the reduced feature vector. Finally, these singular values are introduced into a support vector machine (SVM) classifier to realize fault classification.
Findings
The experiment results show a high fault classification accuracy based on the proposed method.
Originality/value
The proposed approach for rolling bearing fault diagnosis based on SIFT-KPCA and SVM is highly effective in the experiment. The practical value in engineering application of this method can be researched in the future.
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