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On-line neural network fault diagnosis method based on data driving

A neural network and fault diagnosis technology, applied in neural learning methods, biological neural network models, electrical digital data processing, etc., can solve problems such as inability to update databases in real time

Active Publication Date: 2019-02-22
SICHUAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a data-driven online neural network fault diagnosis method to solve the problem that the database cannot be updated in real time in the traditional actuator fault diagnosis

Method used

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  • On-line neural network fault diagnosis method based on data driving
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  • On-line neural network fault diagnosis method based on data driving

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Embodiment

[0081] The present invention will be further described below by way of examples.

[0082] Embodiment provides a kind of online neural network fault diagnosis method based on data drive, and its specific steps are as follows:

[0083] Step 1: Analyze the structure and working form of the actuator, and build the Simulink model and AMEsim model of the actuator. The electro-hydraulic actuator mainly includes a signal processing system, a hydraulic system and a mechanical system, combining their common advantages . The electro-hydraulic servo system can flexibly meet the functions of high control precision, fast response and signal processing. The signal processing part is mainly completed by the PID control system to complete the processing of the manipulation command; the hydraulic part is usually composed of servo valves, solenoid valves, mode switching valves, etc.; the mechanical part is mainly composed of actuators and other executive components. At present, most electro-hy...

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Abstract

The invention relates to the fault diagnosis field of an electro-hydraulic actuator, and discloses an on-line neural network fault diagnosis method based on data driving, which solves the problem thata database cannot be updated in real time in the traditional fault diagnosis of the actuator. The main points of the invention are as follows: analyzing the structure of an electro-hydraulic actuator, and building a simulation model of the electro-hydraulic actuator; analyzing the failure form of actuator, implanting different types of faults into the simulation model of electro-hydraulic actuator to complete the signal acquisition; collecting the data, extracting the eigenvalue and reduce the dimension; constructing the tag fault data matrix, and training the eigenvalues offline and online,so that the online neural network can be obtained. The electro-hydraulic actuator working data is acquired in real time, an on-line neural network is used to collect the data for fault diagnosis; after fault diagnosis, the collected data are trained on-line to update the on-line neural network. The invention is applicable to the fault diagnosis of the electro-hydraulic actuator.

Description

technical field [0001] The invention relates to the field of fault diagnosis of electro-hydraulic actuators, in particular to a data-driven online neural network fault diagnosis method. Background technique [0002] With the rapid development of aerospace industry, robot and civil industry and the continuous improvement of functional requirements, the application range and complexity of control systems are becoming more and more prominent, and the reliability and safe operation of equipment are becoming more and more important. Due to the long-term operation of the equipment and the complexity and variability of the working environment, failures are inevitable. Once the corresponding system or equipment fails under unknown circumstances, it will bring huge property losses and casualties. Therefore, it is necessary to monitor the health status of the equipment and diagnose the faults in real time. When a fault occurs, the diagnosis and identification of the fault can be comp...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/08
CPCG06N3/08G06F30/20
Inventor 苗强王剑宇刘慧宇莫贞凌曾小飞张恒
Owner SICHUAN UNIV
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