Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine
This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-no...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/2/185 |
| _version_ | 1798040627115458560 |
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| author | Jian-Hua Zhong Pak Kin Wong Zhi-Xin Yang |
| author_facet | Jian-Hua Zhong Pak Kin Wong Zhi-Xin Yang |
| author_sort | Jian-Hua Zhong |
| collection | DOAJ |
| description | This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox. |
| first_indexed | 2024-04-11T22:10:18Z |
| format | Article |
| id | doaj.art-d09dc1c2beec4b9b9f4962bdb564a8f8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:10:18Z |
| publishDate | 2016-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d09dc1c2beec4b9b9f4962bdb564a8f82022-12-22T04:00:35ZengMDPI AGSensors1424-82202016-02-0116218510.3390/s16020185s16020185Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee MachineJian-Hua Zhong0Pak Kin Wong1Zhi-Xin Yang2Department of Electromechanical Engineering, University of Macau, Macao, ChinaDepartment of Electromechanical Engineering, University of Macau, Macao, ChinaDepartment of Electromechanical Engineering, University of Macau, Macao, ChinaThis study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox.http://www.mdpi.com/1424-8220/16/2/185simultaneous-fault diagnosisHilbert-Huang transformpairwise-coupling probabilistic committee machine |
| spellingShingle | Jian-Hua Zhong Pak Kin Wong Zhi-Xin Yang Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine Sensors simultaneous-fault diagnosis Hilbert-Huang transform pairwise-coupling probabilistic committee machine |
| title | Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine |
| title_full | Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine |
| title_fullStr | Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine |
| title_full_unstemmed | Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine |
| title_short | Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine |
| title_sort | simultaneous fault diagnosis of gearboxes using probabilistic committee machine |
| topic | simultaneous-fault diagnosis Hilbert-Huang transform pairwise-coupling probabilistic committee machine |
| url | http://www.mdpi.com/1424-8220/16/2/185 |
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