BP neural network-based on fault diagnosis of hydraulic servo-valves

Huang, Hao; Chen, Kuisheng; Zeng, Liangcai

doi:10.1109/icmlc.2005.1527655

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…e method provided an intelligent system to be used in condition monitoring of centrifugal pumps. Huang et al [19] proposed a new method for fault diagnosis hydraulic servo valve based on genetic algorithm for backpropagation neural network. Compared with other artificial neural networks, this method shortens the training time and improves the accuracy.…”

Section: Introductionmentioning

confidence: 99%

Application of Fault Diagnosis of Seawater Hydraulic Pump Based on Transfer Learning

Yang

Jiang

Huang

et al. 2020

Shock and Vibration

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The working environment of seawater axial piston hydraulic pump is harsh, and it is difficult to diagnose due to insufficient fault database. In contrast, pumps of the same type but using hydraulic oil have an adequate fault database and are easy to diagnose. In view of the above situation, a fault diagnosis method of seawater hydraulic piston pump based on transfer learning is proposed. The method decomposes the original sampled fault signal by complementary ensemble empirical mode decomposition (CEEMD) to obtain the intrinsic mode function (IMF) that can characterize the original signal. The singular value decomposition (SVD) is performed on the IMF. Then, the obtained singular value is used as a feature parameter to construct a feature vector. The feature data of seawater hydraulic pump and oil pump are used as target data and auxiliary data to form training data. The training data is trained based on the iterative adjustment of the weight through the TrAdaBoost transfer learning algorithm. Finally, the results of diagnosis and classification are compared with traditional machine learning. When the number of training data is 5 groups, the accuracy of transfer learning is 30.5% higher than that of traditional machine learning. The results show that transfer learning has great advantages in the case of a small number of samples.

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Section: Introductionmentioning

confidence: 99%

Application of Fault Diagnosis of Seawater Hydraulic Pump Based on Transfer Learning

Yang

Jiang

Huang

et al. 2020

Shock and Vibration

View full text Add to dashboard Cite

show abstract

Fault diagnosis of electrohydraulic actuator based on multiple source signals: An experimental investigation

Wang

Miao

Wang³

et al. 2020

Neurocomputing

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An Intelligent Fault Diagnostics for Turbine Generator by Modified Neural Model

Weng

Yang

Liu³

et al. 2008

2008 3rd International Conference on Innovative Computing Information and Control

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In this paper, an intelligent fault diagnostic tool for oil-fired power plant with turbine generator by using the modified neural network was proposed. This tool is able to monitor the running condition of power plant immediately. It also can reveal the fault situation if the power plant had some troubles. Therefore, such a well designed mechanism can be used as the training tool for laboratory course in power turbine studies. To demonstrate the feasibility of the tool we developed, several real case studies were simulated. From the simulation results shown, the tool we developed is very promising in the real applications.

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BP neural network-based on fault diagnosis of hydraulic servo-valves

Cited by 3 publications

References 5 publications

Application of Fault Diagnosis of Seawater Hydraulic Pump Based on Transfer Learning

Application of Fault Diagnosis of Seawater Hydraulic Pump Based on Transfer Learning

Fault diagnosis of electrohydraulic actuator based on multiple source signals: An experimental investigation

An Intelligent Fault Diagnostics for Turbine Generator by Modified Neural Model

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