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Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform

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  • Tang, Shengnan
  • Zhu, Yong
  • Yuan, Shouqi

Abstract

Hydraulic piston pump is known as one of the most critical parts in a typical hydraulic transmission system. It is imperative to probe into an accurate fault diagnosis method to guarantee the stability and reliability of the system. Due to the shortcomings of traditional methods, the development of artificial intelligence enlightens the intensive exploration for machinery fault diagnosis. In this research, a normalized convolutional neural network (NCNN) framework with batch normalization strategy is developed for feature extraction and fault identification. First, the batch normalization technology is introduced in the modeling to resolve the change of data distribution. Second, inspired by the intelligent algorithms, Bayesian algorithm is employed to automatically tune the model hyperparameters. The improved model is named BNCNN. Third, BNCNN is used for fault diagnosis based on synchrosqueesed wavelet transform. The experiments in a hydraulic piston pump are employed for the demonstration of the method. Moreover, the superior performance of the proposed method is validated by the contrastive analysis. The results reveal that BNCNN can accurately and steadily complete the fault classification of hydraulic pump.

Suggested Citation

  • Tang, Shengnan & Zhu, Yong & Yuan, Shouqi, 2022. "Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:reensy:v:224:y:2022:i:c:s0951832022002083
    DOI: 10.1016/j.ress.2022.108560
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    Cited by:

    1. Chao, Qun & Shao, Yuechen & Liu, Chengliang & Yang, Xiaoxue, 2023. "Health evaluation of axial piston pumps based on density weighted support vector data description," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Dong, Yutong & Jiang, Hongkai & Wu, Zhenghong & Yang, Qiao & Liu, Yunpeng, 2023. "Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Liu, Yi & Xiang, Hang & Jiang, Zhansi & Xiang, Jiawei, 2023. "Second-order transient-extracting S transform for fault feature extraction in rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Md. Abu Ayub Siddique & Yong-Joo Kim & Seung-Min Baek & Seung-Yun Baek & Tae-Ho Han & Wan-Soo Kim & Yeon-Soo Kim & Ryu-Gap Lim & Yong Choi, 2022. "Development of the Reliability Assessment Process of the Hydraulic Pump for a 78 kW Tractor during Major Agricultural Operations," Agriculture, MDPI, vol. 12(10), pages 1-15, October.
    5. Grzegorz Filo, 2023. "Artificial Intelligence Methods in Hydraulic System Design," Energies, MDPI, vol. 16(8), pages 1-19, April.
    6. Chen, Jiayu. & Lin, Cuiyin & Yao, Boqing & Yang, Lechang & Ge, Hongjuan, 2023. "Intelligent fault diagnosis of rolling bearings with low-quality data: A feature significance and diversity learning method," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Liu, Jie & Xu, Huoyao & Peng, Xiangyu & Wang, Junlang & He, Chaoming, 2023. "Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Li, Sheng & Ji, J.C. & Xu, Yadong & Sun, Xiuquan & Feng, Ke & Sun, Beibei & Wang, Yulin & Gu, Fengshou & Zhang, Ke & Ni, Qing, 2023. "IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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