Fault Diagnosis Method of the Construction Machinery Hydraulic System Based on Artificial Intelligence Dynamic Monitoring

This paper aims to study the fault diagnosis method of the mechanical hydraulic system based on artificial intelligence dynamic monitoring. According to the characteristics of functional principal component analysis (FPCA) and neural network in the fault diagnosis method in the feature extraction process, the fault diagnosis method combining functional principal component analysis and BP neural network is studied and it is applied to the fault of the coordinator hydraulic system diagnosis. This article mainly completed the following tasks: analyzing the structure and working principle of the mechanical hydraulic system, studying the failure mechanism and failure mode of the mechanical hydraulic system, summarizing the common failures of the hydraulic system and the individual failures of the mechanical hydraulic system, and establishing the mechanical hydraulic system. Description of failure mode and effects analysis (FMEA): then, a joint simulation model of the mechanical hydraulic system was established in ADAMS and AMESim, and the fault detection signal of the hydraulic system was determined and compared with the experimental data. At the same time, the simulation data of the cosimulation model were compared with the simulation data of the hydraulic model in MATLAB to further verify the correctness of the model. The functional principal component analysis is used to perform functional processing on sample data, feature parameters are extracted, and the BP neural network is used to train the mapping relationship between feature parameters and fault parameters. The consistency is verified, and the fault diagnosis method is finally completed. The experimental results show that the diagnostic accuracy rates are 0.9848 and 0.9927, respectively, the reliability is significantly improved, close to 100%, and the uncertainty is basically 0, which significantly improves the accuracy of fault diagnosis.

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A novel bearing fault diagnosis method based on principal component analysis and BP neural network

2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) ◽

10.1109/icemi46757.2019.9101435 ◽

2019 ◽

Author(s):

Sun Yue ◽

Xu Aidong ◽

Wang Kai ◽

Han Xiaojia ◽

Guo Haifeng ◽

...

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Principal Component ◽

Component Analysis ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method

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Analog Circuit Fault Diagnosis Based on Principal Component Analysis of Pretreatment and Particle Swarm Hybrid Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.494-495.809 ◽

2014 ◽

Vol 494-495 ◽

pp. 809-812

Author(s):

Guo Huang ◽

Bao Ru Han

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Fault Diagnosis ◽

Analog Circuit ◽

Particle Swarm ◽

Principal Component ◽

Component Analysis ◽

Hybrid Neural Network ◽

Diagnosis Method ◽

Circuit Fault Diagnosis

With the rapid development of electronic technology, the system reliability and economic requirements of the importance of the analog circuit fault diagnosis has become increasingly prominent. Aiming at the shortcomings of traditional diagnosis method, the paper presents an analog circuit fault diagnosis method based on principal component analysis of pretreatment and particle swarm hybrid neural network. The method adopts hybrid algorithm to adjust the network weights and thresholds to avoid falling into the local minimum value, which uses principal component pretreatment effectively reduce the complexity of calculation. Simulation results show that the diagnostic method can be used for tolerance analog circuit fault diagnosis, has better application prospect.

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