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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Vydané v:Journal of manufacturing systems Ročník 57; s. 148 - 157
Hlavní autori: Cai, Baoping, Sun, Xiutao, Wang, Jiaxing, Yang, Chao, Wang, Zhengda, Kong, Xiangdi, Liu, Zengkai, Liu, Yonghong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.10.2020
Predmet:
Bayesian networks
BP neural networks
diesel engine
fault diagnosis
rule-based algorithm
diesel engine
fault diagnosis
rule-based algorithm
BP neural networks
Bayesian networks
ISSN:0278-6125, 1878-6642
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Shrnutí:•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.
ISSN:0278-6125
1878-6642
DOI:10.1016/j.jmsy.2020.09.001