Fault detection and diagnostic method of diesel engine by combining rule-based algorithm and BNs/BPNNs
•A fault diagnostic method of diesel engines by combining rule-based algorithm and BNs/BPNNs is proposed.•Faults are roughly identified using rule-based algorithm and finely identified using BNs or BPNNs.•The method can identify faults of diesel engines with different rotation speeds when the traini...
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| Published in: | Journal of manufacturing systems Vol. 57; pp. 148 - 157 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.10.2020
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| Subjects: | |
| ISSN: | 0278-6125, 1878-6642 |
| Online Access: | Get full text |
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| Abstract | •A fault diagnostic method of diesel engines by combining rule-based algorithm and BNs/BPNNs is proposed.•Faults are roughly identified using rule-based algorithm and finely identified using BNs or BPNNs.•The method can identify faults of diesel engines with different rotation speeds when the training speed is constant.
The stable operation of diesel engine is critical to the normal production of the industry, and the prevention, monitoring, and identification of faults are of great significance. At present, the fault research on diesel engines still has some defects, such as only few types of faults diagnosis are identified, the accuracy of fault diagnosis is still low, and fault identification is located at a fixed speed. A novel fault detection and diagnostic method of diesel engine by combining rule-based algorithm and Bayesian networks (BNs) or Back Propagation neural networks (BPNNs) is proposed. The signals are processed by wavelet threshold denoising and ensemble empirical mode decomposition. The signal-sensitive feature values are extracted from the decomposed intrinsic mode function. Seven faults are roughly identified using rule-based algorithm and finely identified using BNs or BPNNs. Results show the proposed fault diagnosis method has a good diagnostic performance for a wide range of rotation speeds when the training data for BNs and BPNNs are from fixed speeds. In addition, the influences of the layers of decomposed signals, sensor noise and external excitation interference on the fault diagnostic performance are also researched. |
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| AbstractList | •A fault diagnostic method of diesel engines by combining rule-based algorithm and BNs/BPNNs is proposed.•Faults are roughly identified using rule-based algorithm and finely identified using BNs or BPNNs.•The method can identify faults of diesel engines with different rotation speeds when the training speed is constant.
The stable operation of diesel engine is critical to the normal production of the industry, and the prevention, monitoring, and identification of faults are of great significance. At present, the fault research on diesel engines still has some defects, such as only few types of faults diagnosis are identified, the accuracy of fault diagnosis is still low, and fault identification is located at a fixed speed. A novel fault detection and diagnostic method of diesel engine by combining rule-based algorithm and Bayesian networks (BNs) or Back Propagation neural networks (BPNNs) is proposed. The signals are processed by wavelet threshold denoising and ensemble empirical mode decomposition. The signal-sensitive feature values are extracted from the decomposed intrinsic mode function. Seven faults are roughly identified using rule-based algorithm and finely identified using BNs or BPNNs. Results show the proposed fault diagnosis method has a good diagnostic performance for a wide range of rotation speeds when the training data for BNs and BPNNs are from fixed speeds. In addition, the influences of the layers of decomposed signals, sensor noise and external excitation interference on the fault diagnostic performance are also researched. |
| Author | Kong, Xiangdi Cai, Baoping Liu, Yonghong Yang, Chao Wang, Zhengda Sun, Xiutao Wang, Jiaxing Liu, Zengkai |
| Author_xml | – sequence: 1 givenname: Baoping orcidid: 0000-0002-4499-492X surname: Cai fullname: Cai, Baoping email: caibaoping@upc.edu.cn organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong, 266580, China – sequence: 2 givenname: Xiutao surname: Sun fullname: Sun, Xiutao organization: College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China – sequence: 3 givenname: Jiaxing surname: Wang fullname: Wang, Jiaxing organization: CRRC Qingdao Sifang Rolling Sock Research Institute CO., LTD., Qingdao, Shandong, 266033, China – sequence: 4 givenname: Chao surname: Yang fullname: Yang, Chao organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong, 266580, China – sequence: 5 givenname: Zhengda surname: Wang fullname: Wang, Zhengda organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong, 266580, China – sequence: 6 givenname: Xiangdi surname: Kong fullname: Kong, Xiangdi organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong, 266580, China – sequence: 7 givenname: Zengkai surname: Liu fullname: Liu, Zengkai organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong, 266580, China – sequence: 8 givenname: Yonghong surname: Liu fullname: Liu, Yonghong organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong, 266580, China |
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