Abstract
The fault diagnosis of rolling element bearings is very important for ensuring the safe operation of rotary machineries. Targeting the nonstationary characteristics of the vibration signals of rolling element bearings, a novel approach based on dual-tree complex wavelet packet transform, improved intrinsic time-scale decomposition, and the online sequential extreme learning machine is proposed in this article for the fault recognition of rolling element bearing. First, the feature extraction method of the measured signal is presented by combining improved intrinsic time-scale decomposition with dual-tree complex wavelet packet transform as preprocessor and two-step screening processes based on the energy ratio, the vibration signal is adaptively decomposed into a set of proper rotation components; second, the matrix formed by different proper rotation components and singular value decomposition is used to obtain singular value as eigenvector; finally, singular values are input to online sequential extreme...
Highlights
The assessment of the working condition and the fault identification are critically important to make sure the safe operation of the rolling element bearing in rotating machine
The vibration signal is adaptively decomposed into a number of proper rotation components (PRCs) by dual-tree complex wavelet packet transform (DTCWPT)-intrinsic time-scale decomposition (IITD); two-step screening processes based on the energy ratio are introduced to carry out the screening of PRCs and remove meaningless feature components
Through analyzing the desired PRCs obtained by DTCWPT-IITD, it is found that these PRCs contain main working condition information
Summary
The assessment of the working condition and the fault identification are critically important to make sure the safe operation of the rolling element bearing in rotating machine. Bearing fault detection can be undertaken using different information carriers such as vibration signals, lubricant information, and acoustic and Qingbin Tong and Junci Cao are co-first authors contributed to this work. Advances in Mechanical Engineering temperature data.[1] Among them, the vibration signal contains abundant fault information. Vibration-based analysis is widely used for diagnosing fault of rolling element bearing.[2] Numerous analytical methods based on vibration have been presented in the literature for the fault diagnosis of rolling element bearing, which cover time domain and frequency domain.[3] In addition, other solution has been proposed by Villa for bearings diagnostic test.[4] the characteristic of the vibration signal of rolling element bearings is nonlinearity and nonstationarity, which makes it very difficult to clearly detect the fault of the rolling element bearing only in the time domain or the frequency domain.[5]
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