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| Fault diagnosis performance optimization method based on decorrelation multi-frequency EMD |
| ZHAN Yingyu1,2, CHENG Lianglun1,2, WANG Tao2 |
1. School of Computing, Guangdong University of Technology, Guangzhou 510006, China;
2. Guangdong Provincial Key Lab of Cyber-Physical Fusion System, Guangzhou 510006, China |
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Abstract Aiming at the problem of modal aliasing caused due to using EMD in fault diagnosis leading to lower fault feature extraction accuracy, a method based on decorrelation multi-frequency empirical mode decomposition (DMFEMD) was proposed here. Firstly, multi-frequency masking signals were added into the original signal, the latter was decomposed into several components with different frequency ratios to obtain multiple intrinsic mode functions (IMFs). Secondly, the correlation coefficient between adjacent IMFs was calculated, two IMFs were decoupled, and the mixed parts of IMFs were separated to obtain the optimal IMF. Finally, this optimal IMF was subtracted from the original signal, and the above steps repeated until the residual part became a constant or monotonic. Thus, the obtained optimal IMFs were uncorrelated and not interfered with each other, the modal aliasing was significantly weakened, and the fault feature extraction accuracy was effectively improved. A feature sample set was constructed using the permutation entropy (PE) algorithm for the obtained optimal IMFs. A SVM was introduced to build a fault classification model, and realize equipment fault diagnosis. Tests showed that compared with the traditional method, DMFEM can be used to effectively separate mixed signals with different frequency ratios, and improve the decomposition effect; taking faulty vibration signal of bearing as an example, DMFEMD can be used to better extract bearing fault features; PE combined with SVM can realize efficient and accurate diagnosis of different fault types.
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Received: 11 May 2018
Published: 28 December 2019
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