A Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition

Document Type : Original Article

Authors

Department of Artillery Engineering, Army Engineering University, He Ping Road, Shijiazhuang, China

Abstract

In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method in the signal separation, using the morphological difference of the components in the automatic vibration signal, different sparse dictionaries were constructed to separate the components, eliminates the noise components and extracted the effective fault characteristic component, the extracted impact components are decomposed by EEMD and the energy feature of each IMF component is calculated as the fault features, then put the fault features into SVM (Support Vector Machine) and identify the faults. Through the construction simulation example and the typical fault simulation test of automatic machine, it showed that the morphological component analysis method had better noise reduction and signal separation effect. Compared with the traditional EEMD method, the feature extraction method based on the MCA-EEMD can distinguish automaton fault types more effectively.

Keywords


 
1. Zaza, G., Hammou, A., Benchatti, A., and Saiah, H.,  “Fault
Detection Method on a Compressor Rotor Using the Phase
Variation of the Vibration Signal”, International Journal of
Engineering - Transactions B: Applications, Vol. 30, No. 8,
(2017), 1176–1181.  
2. Zhang, Y. X., Pan, H. X., and An, B., “Automaton fault diagnosis
based on EEMD and FCM clustering”, Journal of Henan
University of Science and Technology, Vol. 38, No. 1, (2017),
17–24.  
3. Pan, H., Yunpeng, C., and Hairui, W., “Study on Automaton Fault
Diagnosis Based on Chaos Theory”, Journal of Gun Launch &
Control, Vol. 35, No. 2, (2014), 50–54.  
4. Cao, M. and Pan, H., “Automaton Intelligent Fault Diagnosis
Based on The Second Generation of Wavelet Transform and
Probabilistic Neural Networks”, Machine Design and Research,
Vol. 31, No. 3, (2015), 22–26.  
5. Cao, M., Pan, H., and Chang, X., “Research on automatic fault
diagnosis based on time -frequency characteristics and PCASVM”,

In 2016 13th International Conference on Ubiquitous
Robots and Ambient Intelligence (URAI), IEEE, (2016), 593–598.  
6. Starck, J.L., Moudden, Y., Bobin, J., Elad, M., and Donoho, D.L.,
“Morphological component analysis”, In Proceedings of Society
of Photo-Optical Instrumentation Engineers (SPIE), Vol. 5914,
(2005), 1-15.  
7. Starck, J., Donoho, D., and Elad, M., “Redundant multiscale
transforms and their application for morphological component
separation”, No. DAPNIA-2004-88, CM-P00052061, france,
(2004).
8. Li, H., Zheng, H. and Tang, L., “Application of morphological
component analysis to gearbox compound fault diagnosis”,
Journal of Vibration, Measurement and Diagnosis, Vol. 33, No.
4, (2013), 620–626.  
9. Chen, X., Yu, D., Li, R., “Compound fault diagnosis method for
gearbox based on morphological component analysis and order
tracking”, Journal of Aerospace Power, Vol. 29, No. 1, (2014),
225–232.  
10. Xu, Y.G., Zhao, G.L., Ma, C.Y., and Hou, S.F., “Denoising
method based on dual-tree complex wavelet transform and MCA
and its application in gear fault diagnosis”, Journal of Aerospace
Power, Vol. 31, No. 1, (2016), 219–226.  
11. Huang, N.E., Shen, Z., Long, S.R., Tung, C.C., and Liu, H.H.,
“The empirical mode decomposition and the Hilbert spectrum for
nonlinear and non-stationary time series analysis”, Proceedings of
the Royal Society of London. Series A: Mathematical, Physical
and Engineering Sciences, Vol. 454, No. 1971, (1998), 903–995.  
12. Gao, H.Y. and Bruce, A.G., “WaveShrink with firm shrinkage”,
Statistica Sinica, Vol. 7, No. 4, (1997), 855–874.  
13. Zhang, X. and Zhou, J., “Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and
optimized support vector machines”, Mechanical Systems and
Signal Processing, Vol. 41, No. 1–2, (2013), 127–140.  
14. Yektaniroumand, T., Azari, M.N., and Gholami, M., “Optimal
Rotor Fault Detection in Induction Motor Using Particle-Swarm
Optimization Optimized Neural Network. ”, International
Journal of Engineering - Transactions B: Applications, Vol. 31,
No. 11, (2018), 1876–1882. .