Abstract
A novel bearing vibration signal fault feature extraction and recognition method based on the improved local mean decomposition (LMD), permutation entropy (PE) and the optimized K-means clustering algorithm is put forward in this paper. The improved LMD is proposed based on the self-similarity of roller bearing vibration signal extending the right and left side of the original signal to suppress its edge effect. After decomposing the extended signal into a set of product functions (PFs), the PE is utilized to display the complexity of the PF component and extract the fault feature meanwhile. Then, the optimized K-means algorithm is used to cluster analysis as a new pattern recognition approach, which uses the probability density distribution (PDD) to identify the initial centroid selection and has the priority of recognition accuracy compared with the classic one. Finally, the experiment results show the proposed method is effectively to fault extraction and recognition for roller bearing.
Highlights
Roller bearings are the most common parts and play the key role in rotating machinery system.Under the working conditions of high-speed and heavy-load, varying degrees of failures always appear in different locations of bearings which are probably related almost 50% of all motor faults [1].In order to monitor the health condition of bearings, the vibration-based signal processing techniques are seen as the most valid methods for diagnosing the roller bearing faults due to vibration signals accompanies with a lot of useful information of failures [2,3]
Once permutation entropy (PE) is taken as a feature factor to extract the fault information from the vibration signals, the obtained features are fed into optimized K-means to fault pattern recognition in this paper
After fault features are extracted by improved local mean decomposition (LMD) and PE, it is necessary to classify the condition of the roller bearings
Summary
Roller bearings are the most common parts and play the key role in rotating machinery system. EMD could decompose a complicated signal into several intrinsic mode functions (IMFs) and verify the basic oscillation mode of the effective signal in essence by combining with Hilbert transform It is precisely because of this combination that it has many problems such as boundary effect, mode mixing and over and undershoot problems. In order to extract the representation information of faults, permutation entropy (PE) is proposed to measure it [18] It has been successfully and widely applied in the signal processing because PE highlights the simplicity, robustness and reduces computational cost [19]. Once PE is taken as a feature factor to extract the fault information from the vibration signals, the obtained features are fed into optimized K-means to fault pattern recognition in this paper.
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