Class-incremental continual learning model for plunger pump faults based on weight space meta-representation

Liu, Siyuan; Huang, Jinying; Ma, Jiancheng; Luo, Jia

doi:10.1016/j.ymssp.2023.110309

Cited by 17 publications

(2 citation statements)

References 25 publications

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“…This method solves the issue of nonintuitive time-domain waveform failure signals in HPPs and intuitively reflects the difference between the failure type and the normal HPP type, as well as the characteristics of the fault state [80]. Liu et al [81] presented a continuous learning model based on a combination of weight space meta-representation with a modified WaveletKernelNet. This model adapts to the dynamic changes in failure types and repeatedly updates the diagnostic model.…”

Section: Combined With Artificial Neural Networkmentioning

confidence: 99%

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

Zhu,

Wu,

Tang

et al. 2023

JMSE

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As the modern industry rapidly advances toward digitalization, networking, and intelligence, intelligent fault diagnosis technology has become a necessary measure to ensure the safe and stable operation of mechanical equipment and effectively avoid major disaster accidents and huge economic losses caused by mechanical equipment failure. As the “power heart” of hydraulic transmission systems, hydraulic piston pumps (HPPs) occupy an important position in aerospace, navigation, national defense, industry, and many other high-tech fields due to their high-rated pressure, compact structure, high efficiency, convenient flow regulation, and other advantages. Faults in HPPs can create serious hazards. In this paper, the research on fault recognition technology for HPPs is reviewed. Firstly, the existing fault diagnosis methods are described, and the typical fault types and mechanisms of HPPs are introduced. Then, the current research achievements regarding fault diagnosis in HPPs are summarized based on three aspects: the traditional intelligent fault diagnosis method, the modern intelligent fault diagnosis method, and the combined intelligent fault diagnosis method. Finally, the future development trend of fault identification methods for HPPs is discussed and summarized. This work provides a reference for developing intelligent, efficient, and accurate fault recognition methods for HPPs. Moreover, this review will help to increase the safety, stability, and reliability of HPPs and promote the implementation of hydraulic transmission technology in the era of intelligent operation and maintenance.

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Section: Combined With Artificial Neural Networkmentioning

confidence: 99%

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

Zhu,

Wu,

Tang

et al. 2023

JMSE

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“…Liu et al [12] proposed a continuous learning model based on weight space element representation to apply to the incremental fault diagnosis of point machine plunger pumps. Wei X. et al [13] proposed a cavitation fault diagnosis method based on the combination of long short-term memory (LSTM) and a one-dimensional convolutional neural network (1D-CNN) to identify the cavitation grade of vibration signals under different inlet pressures.…”

Section: Introductionmentioning

confidence: 99%

The Fault Diagnosis of a Plunger Pump Based on the SMOTE + Tomek Link and Dual-Channel Feature Fusion

Yang,

Xu,

Wang

et al. 2024

Applied Sciences

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Mechanical condition monitoring data in real engineering are often severely unbalanced, which can lead to a decrease in the stability and accuracy of intelligent diagnosis methods. In this paper, a fault diagnosis method based on the SMOTE + Tomek Link and dual-channel feature fusion is proposed to improve the performance of the sample imbalance fault diagnosis method, taking the piston pump of a turnout rutting machine as the research object. Combining the data undersampling method and the oversampling method to redistribute the collected normal data and fault data makes the diagnostic model have better diagnostic performance in the case of insufficient fault samples. And, in order to fully utilize the global features and local features, a global–local feature complementary module (GLFC) is proposed. Firstly, the generated data similar to the original data are constructed using the SMOTE + Tomek Link method; secondly, the generated data are input into a GLFC module and BiGRU at the same time, the GLFC module extracts the spatial global features and local features of the original vibration data, and BiGRU extracts the temporal information features of the original vibration data, and fuses the extracted feature information, and inputs the fused features into the attention layer; finally, a GLFC module is proposed by the SMOTE + Tomek Link method to make full use of the global features and local features. The extracted feature information is fused, and the fused features are input to the attention layer; finally, the fault classification is completed by the softmax classifier. In this paper, the accuracy and robustness of the proposed model are demonstrated through experiments.

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