Subsequence Time Series Clustering-Based Unsupervised Approach for Anomaly Detection of Axial Piston Pumps

Dong, Chang; Tao, Jianfeng; Chao, Qun; Yu, Honggan; Liu, Chengliang

doi:10.1109/tim.2023.3264045

Cited by 11 publications

(1 citation statement)

References 35 publications

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“…In the absence of a large number of labeled data, Dong et al put forward an unsupervised anomaly detection method for piston pumps based on subsequence time-series clustering. Simultaneously, it can also identify weak fault data; enhancing the generalization ability of this means, under variable external loads [49]. Furthermore, intelligent optimization algorithms have been introduced for selecting the critical model hyperparameters (HPs) and broadening the direction for fault pattern recognition of an axial piston pump, such as random search, adaptive search, artificial intelligence optimization and swarm intelligence optimization [50][51][52].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump

Zhu

Zhou

Tang

et al. 2023

JMSE

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The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump.

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