Abstract
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.
Highlights
Robot technology has the following characteristics: first, it has a high degree of freedom and a strong ability to control the system
Simulation results show that the multifault diagnosis method based on the extended Kalman filter is effective for multifault diagnosis of electrohydraulic servo drive systems [1, 2]. e naval gun weapon system is a complex large-scale mechatronic system composed of the mechanical system, electrical system, hydraulic system, and so on. e system has a wide working range, poor working environment, and high failure rate
In order to verify the performance of the trained four neural networks, the historical fault data of the mechanical hydraulic system at certain three moments are processed as test samples, and the subneural networks constructed above are used for diagnosis
Summary
Robot technology has the following characteristics: first, it has a high degree of freedom and a strong ability to control the system. There are the following areas: (1) expert system is a knowledge system built on the existing knowledge of human experts It applies artificial intelligence technology and simulates the thinking process of human experts when solving problems to solve various problems in the field. Simulation results show that the multifault diagnosis method based on the extended Kalman filter is effective for multifault diagnosis of electrohydraulic servo drive systems [1, 2]. In the research of artificial intelligence, an artificial neural network is a good method that can solve many problems in fault diagnosis. Due to the effectiveness of artificial intelligence methods can artificial neural network methods be used to study the fault diagnosis methods of mechanical hydraulic systems and solve the problem of insufficient accuracy of current fault diagnosis.
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