A data indicator-based deep belief networks to detect multiple faults in axial piston pumps. (November 2018)

• Record Type: Journal Article
• Authors: Wang, Shuhui; Xiang, Jiawei; Zhong, Yongteng; Tang, Hesheng
• Is Part Of: Mechanical systems and signal processing. Volume 112(2018)
• Page Start: 154
• Page End: 170
• Publication Date: 2018-11
• Subjects: Piston pumps -- Multiple faults classification -- Deep belief networks -- Data indicators -- Feature learning; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.04.038
• Languages: English
• ISSNs: 0888-3270

Abstract

Graphical abstract: Highlights: A data indicator-based DBNs is developed to detect multiple faults. The combination of 27 indicators is utilized to generate training and testing samples. Both numerical simulations and experimental investigations are performed. Four faults in axial piston pumps are classified with 97.40% accuracy ratio. Abstract: Detecting faults in axial piston pumps is of significance to enhance the reliability and security of hydraulic systems. However, it is difficult to detect multiple faults in the hydraulic electromechanical coupling systems because the fault mechanism of some faults is unclear. In this paper, a method using deep belief networks (DBNs) is proposed to detect multiple faults in axial piston pumps. Firstly, for each individual fault, all the data indicators extracted from the raw signals in time domain, frequency domain and time-frequency domain are calculated to construct training and testing samples. Then, the constructed samples are fed into DBNs to classify the multiple faults in axial piston pumps. With restricted Boltzmann machine (RBM) stacked layer by layer, DBNs can automatically learn fault features. Numerical simulations using the benchmark data of five faults in rolling bearings are classified by the present method to select the relative optimal combination of indicators. The classification results are also compared with those commonly used support vector machine (SVM) and artificial neural network (ANN) to manifest the classification accuracy of the present method. Experimental investigations are performed to classify four faults in an axial piston pump. The classification accuracy ratio is 97.40%, which confirms the feasibility and effectiveness of multiple faults detection in axial piston pumps using DBNs.