Abstract
When fault such as pit failure arises in the rolling element bearing the vibration signal of which will take on periodic characteristics, and the abrupt failure of rotating machinery can be avoided effectively if the weak periodic characteristics of the early fault stage is extracted timely. However, the periodic characteristics of bearing’ early weak fault is hard to be extracted usually and the reasons can be boiled to as following: Firstly, the weak periodic signal of rolling element bearing’ early fault stage is buried by the strong background noise. Secondly, the weak fault cannot show the complete shock attenuation impulsive characteristic due to its weak energy, so the traditional wavelet transform would not work effectively if a proper wavelet basis function fitting for analyzing the impulsive characteristics is not selected. To solve the above two problems, a feature extraction method of the weak periodic signal of rolling element bearing’ early fault based on Shift Invariant Sparse Coding (SISC) originating from sparse representation is proposed in the paper. To capture the underlying structure of machinery fault signal, SICS provides an effective basis functions learning scheme by solving the flowing two convex optimization problems iteratively: 1) L1-regularized least squares problem. 2) L2-constrained least squares problem. The fault feature can be probably contained and extracted if optimal latent component is filtered among these basis functions. The feasibility and effectiveness of the proposed method are verified through the corresponding simulation and experiment.
Highlights
There are safe and economic significances in extracting the fault feature of rolling element bearing opportunely for its wide range using in rotating machinery
To solve the analyzed shortcomings of current fault diagnosis methods, a new fault diagnosis method of rotating machinery by combining wavelet packet decomposition (WPD) with empirical mode decomposition (EMD) was proposed in the paper [1]: the WPD was used as de-noising purpose and the EMD was used as fault feature extraction technique
The overall flow chart of the proposed method is shown in Fig. 1 which can be divided into two steps mainly: Step 1: Fault feature learning from machinery fault signal: The access of the redundant dictionary usually requires a set of standard training signals through learning
Summary
There are safe and economic significances in extracting the fault feature of rolling element bearing opportunely for its wide range using in rotating machinery. An integrated method combing resonance demodulation with entropy threshold de-noising of wavelet packet coefficients was proposed in paper [6] to solve the difficulty of fault feature extraction of the early weak impulsive signal. The validity and effectiveness of the proposed method in feature extraction of rolling element bearing’ early weak impulsive signal were proved by the analysis results of experiment data. The virtues of tunable Q-factor wavelet transform (TQWT) and neighboring coefficient de-noising were combined in paper [7] to propose a de-nosing method of the rolling element bearing’ early weak fault signal corrupted by strong background noise. Though considerable results are achieved, most of the above cited papers obtained the test damaged rolling element bearing with different size using electrical discharge machining (EDM) technology to simulate the early weak fault.
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