Fault location method based on residual and double-stage Elman neural network for hydraulic servo system

A technology of hydraulic servo system and neural network is applied in the field of fault diagnosis of hydraulic servo system, which can solve problems such as difficult fault location.

Inactive Publication Date: 2012-10-03
BEIHANG UNIV
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that the commonly used fault observer based on neural network can realize fault detection,

Method used

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  • Fault location method based on residual and double-stage Elman neural network for hydraulic servo system
  • Fault location method based on residual and double-stage Elman neural network for hydraulic servo system
  • Fault location method based on residual and double-stage Elman neural network for hydraulic servo system

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Embodiment

[0120] This example uses the experimental data of the hydraulic servo system for verification. The sample signals under the three states of normal operation of the hydraulic servo system, electronic amplifier failure and leakage failure are respectively used to detect and verify the fault location method of the hydraulic servo system based on the combination of the residual error and the two-stage Elman neural network of the present invention. The specific steps are as follows:

[0121] Step 1: Acquiring system input and output signals under three states: normal operation of the hydraulic servo system, electronic amplifier failure and leakage failure.

[0122] When the hydraulic servo system is running normally, the input command of the given system is a sinusoidal signal with a frequency of 1 Hz and an amplitude of 20 mm, and 20 sets of system input and output signals are collected under the normal state of the system. Modify the proportional coefficient K of the PID controll...

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Abstract

The invention discloses a fault location method based on a residual and a double-stage Elman neural network for a hydraulic servo system, comprising the following steps of: obtaining the input/output signals of the hydraulic servo system in a normal working state, an electronic amplifier fault state and a leakage fault state, training a fault observer by virtue of the input/output signal in the normal state, and obtaining a real-time residual signal by the fault observer at first, and then training a state follower in real time and on line to obtain a network connection weight corresponding to the real-time signal, and training an RBF (radial basis function) fault locator by using the time-domain characteristic value of the residual signal and the network connection weight as the training input samples of the RBF fault locator. Both of the fault observer and the state follower are realized by the improved Elman network. Whether the system has a fault or not at present can be judged by comparing the time-domain characteristic value with a fault threshold, and the type of the fault can be obtained by the fault locator. The fault location method disclosed by the invention realizes fault location for the hydraulic servo system, and has high location accuracy and engineering applicability.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of a hydraulic servo system, and in particular relates to a fault location method of a hydraulic servo system based on the combination of a residual error and a two-stage Elman neural network. Background technique [0002] In the field of aerospace, hydraulic systems are widely used as the execution link. The hydraulic system has the advantages of high power and fast response, and with the increasing scale, function, complexity and automation level, the hydraulic system is required to have high reliability and maintainability. To this end, a series of measures have been taken to improve the reliability of hydraulic components, high reliability and fault-tolerant design of the system, but no matter how high the reliability of the hydraulic system is, due to many inevitable factors such as load, working conditions, and aging of components, etc. , the hydraulic system will inevitably fail. ...

Claims

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Application Information

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IPC IPC(8): F15B19/00
Inventor 刘红梅刘大伟王靖吕琛欧阳平超
Owner BEIHANG UNIV
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