A Variational Stacked Autoencoder with Harmony Search Optimizer for Valve Train Fault Diagnosis of Diesel Engine

Diesel engine fault diagnosis is vital due to enhanced reliability and economic efficiency requirements. The extracted features in traditional fault diagnosis are constructed manually, which is very cumbersome because of the requirement for lots of expertise. To handle this issue, this paper propose...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 20; no. 1; p. 223
Main Authors: Chen, Kun, Mao, Zhiwei, Zhao, Haipeng, Jiang, Zhinong, Zhang, Jinjie
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 31.12.2019
MDPI
Subjects:
Algorithms
autoencoder
Decomposition
deep learning
diesel engine
Diesel engines
Fault diagnosis
harmony search optimizer
Optimization
Principal components analysis
Regularization methods
Signal processing
Vibration
Wavelet transforms
deep learning
diesel engine
fault diagnosis
autoencoder
harmony search optimizer
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Diesel engine fault diagnosis is vital due to enhanced reliability and economic efficiency requirements. The extracted features in traditional fault diagnosis are constructed manually, which is very cumbersome because of the requirement for lots of expertise. To handle this issue, this paper proposed a variational stacked autoencoder (VSAE) to adaptively extract features from angular domain signals. As an unsupervised algorithm, VSAE can extract high-level features with the help of multiple encoding layers. Layer-wise pre-training and fine-tuning are introduced to get a better network initialization value. Moreover, the dropout technique and the batch normalization technique are carried out to prevent over-fitting and implement fast convergence. Finally, the harmony search optimizer (HSO) algorithm is introduced to get an appropriate hyper-parameter setting in the VSAE model, as well as make adaptive adjustment of the network structure. In order to verify the proposed method, the valve train fault data is collected on the diesel engine test rig under twelve operating conditions. The results indicate that the proposed scheme can effectively diagnose different degrees of intake valve fault, exhaust valve fault, and coupling fault under various operating conditions. Furthermore, the classification accuracy improved from 94.10% to 98.85%VSAE compared with stacked autoencoder (SAE) and some other traditional fault diagnosis algorithms.
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This Paper is an Expanded Version of “Valve Fault Diagnosis of Internal Combustion Engine Based on An Improved Stacked Autoencoder” in the Proceedings of the SDPC 2019, Beijing, China, 15–17 August 2019.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20010223