A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images. (April 2022)

• Record Type: Journal Article
• Authors: Tang, Shengnan; Zhu, Yong; Yuan, Shouqi
• Is Part Of: Advanced engineering informatics. Volume 52(2022)
• Publication Date: 2022-04
• Subjects: Hydraulic piston pump -- Intelligent fault diagnosis -- Convolutional neural network -- Bayesian optimization -- Continuous wavelet transform; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101554
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: An adaptive deep CNN is constructed for fault diagnosis of hydraulic piston pump. The images of acoustic signals by continuous wave transform are taken as input. Bayesian algorithm is introduced for automatic learning of model hyperparameters. The model presents higher accuracy and better robustness in fault identification. This research provides a basis for ensuring the reliability of hydraulic pump. Abstract: As an essential part of hydraulic transmission systems, hydraulic piston pumps have a significant role in many state-of-the-art industries. Thus, it is important to implement accurate and effective fault diagnosis of hydraulic piston pumps. Owing to the heavy reliance of shallow machine learning models on the expertise and experience of engineers, fault diagnosis based on deep models has attracted significant attention from academia and industry. To construct a deep model with good performance, it is necessary and challenging to tune the hyperparameters (HPs). Since many existing methods focus on manual tuning and use common search algorithms, it is meaningful to explore more intelligent algorithms that can automatically optimize the HPs. In this paper, Bayesian optimization (BO) is employed for adaptive HP learning, and an improved convolutional neural network (CNN) is established for fault feature extraction and classification in a hydraulic piston pump. First, acoustic signals are transformed into time–frequency distributions by a continuous wavelet transform. Second, a preliminary CNN model is built by setting initial HPs. The range of each HP to be optimized is identified. Third, BO is employed to select the optimal combination of HPs. An improved model called CNN-BO is constructed. Finally, the diagnostic efficiency of CNN-BO is analyzed using a confusion matrix and t -distributed stochastic neighbor embedding. The classification performance of different models is compared. It is found that CNN-BO has a higher accuracy and better robustness in fault diagnosis for a hydraulic piston pump. This research will provide a basis for ensuring the reliability and safety of the hydraulic pump.