Intelligent fault diagnosis of hydraulic piston pump combining improved LeNet-5 and PSO hyperparameter optimization

Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

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An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

Sensors ◽

10.3390/s20226576 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6576

Author(s):

Shengnan Tang ◽

Shouqi Yuan ◽

Yong Zhu ◽

Guangpeng Li

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Image Features ◽

Learning Ability ◽

Piston Pump ◽

Axial Piston Pump ◽

Intelligent Fault Diagnosis ◽

Time Frequency ◽

Hydraulic Axial Piston Pump ◽

Axial Piston

A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.

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Layered clustering multi-fault diagnosis for hydraulic piston pump

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2012.10.020 ◽

2013 ◽

Vol 36 (2) ◽

pp. 487-504 ◽

Cited By ~ 54

Author(s):

Jun Du ◽

Shaoping Wang ◽

Haiyan Zhang

Keyword(s):

Fault Diagnosis ◽

Piston Pump ◽

Hydraulic Piston Pump

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An improved convolutional neural network with an adaptable learning rate towards multi-signal fault diagnosis of hydraulic piston pump

Advanced Engineering Informatics ◽

10.1016/j.aei.2021.101406 ◽

2021 ◽

Vol 50 ◽

pp. 101406

Author(s):

Shengnan Tang ◽

Yong Zhu ◽

Shouqi Yuan

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Learning Rate ◽

Piston Pump ◽

Hydraulic Piston Pump

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Construction of a deep sparse filtering network for rotating machinery fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211014852 ◽

2021 ◽

pp. 095440702110148

Author(s):

Chun Cheng ◽

Wei Zou ◽

Weiping Wang ◽

Michael Pecht

Keyword(s):

Fault Diagnosis ◽

Back Propagation ◽

Feature Learning ◽

Back Propagation Neural Network ◽

Rotating Machinery ◽

Fine Tuning ◽

Local Optimum ◽

Back Propagation Algorithm ◽

Intelligent Fault Diagnosis ◽

Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

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