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BP neural network model based fault prediction method and system

A BP neural network and fault prediction technology, applied in general control systems, control/regulation systems, instruments, etc., can solve problems such as reducing the reliability of radar systems, failing to repair and maintain in time, and failing to obtain timely

Active Publication Date: 2019-06-07
WEST ANHUI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] When the status parameters of the digital transceiver components are abnormal, maintenance personnel cannot obtain this information in time, and thus cannot repair and maintain in time, resulting in the digital transceiver components working in an abnormal state and reducing the reliability of the radar system

Method used

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  • BP neural network model based fault prediction method and system
  • BP neural network model based fault prediction method and system
  • BP neural network model based fault prediction method and system

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Experimental program
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Effect test

Embodiment 1

[0072] Such as figure 1 As shown, a kind of this prediction system comprises the data collection module 1 that is used to collect state parameter, the data transmission module 2 that is used to transmit state parameter and monitoring management module 3, and described data collection module 1 sends data transmission module to data transmission module through its communication interface 2. Send the data of the state parameters, and the data transmission module 2 sends the data to the monitoring and management module 3 through its network interface.

[0073] The data acquisition module 1 obtains the internal temperature of the digital component through the temperature acquisition unit 11, and the temperature acquisition unit 11 is a temperature sensor; the internal test unit 12 of the data acquisition module 1 obtains the analog operating voltage through a shunt, and the analog-to-digital converter converts the analog operating voltage converted to a digital voltage. The built-...

Embodiment 2

[0077] On the basis of Embodiment 1, the sample data obtained by the monitoring and management module 3 is input into the model based on the BP neural network to calculate the predicted value of the state parameter. In this embodiment, taking the working voltage of the digital transceiver component as an example, the predicted value of the working voltage is calculated, and the potential failure of the digital transceiver component is predicted according to the predicted value of the working voltage. The steps for calculating the predicted value of the state parameter are as follows:

[0078] S1, normalize the sample data of the state parameter of working voltage

[0079]

[0080] Among them, a i is the i-th sample data of the working voltage, n is the total number of sample data of the working voltage, max(a i ) and min(a i ) are the maximum and minimum values ​​of the working voltage respectively, xi is the normalized i-th sample data.

[0081] S2, establish the topolo...

Embodiment 3

[0093] The BP neural network module using particle swarm optimization to calculate the weight w ij 、w jk and threshold θ j and θ k . Include the following steps:

[0094] S1 determines the individual particle dimension D of the particle swarm:

[0095] D=m×f+f×g+f+g (6)

[0096] The dimension D is the total number of weights and thresholds of the BP neural network model, and the dimension D=55 in this embodiment.

[0097] S2. Initialize the velocity and position of the particles in the particle swarm optimization algorithm, and randomly generate the initial velocity and initial position of the particles in an interval. The initial position is the initial value of the weight and threshold. In this embodiment, the initial position is [-10, 10], the initial speed is a random number in [-1,1].

[0098] S3, using the error function of the BP neural network model as the particle swarm adaptation function E 1 :

[0099]

[0100] Among them, s is the total number of traini...

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Abstract

The invention relates to relates to a BP neural network model based fault prediction method and system, and belongs to the technical field of fault prediction of the radar system. The fault predictionmethod comprises that state parameter normalized sample data of a transmitting-receiving assembly is input to a BP neural network model, a predicted value of a state parameter is calculated, comparison analysis is carried out on predicted and reference values of the state parameter, and a fault prediction result is output. The BP neural network model is used to predict the state parameter of a digital transmitting-receiving assembly, the state parameter value in next time can be predicted on the basis of existing sample data, the fault prediction result is obtained conveniently and rapidly, amaintenance staff can know the work state of the digital transmitting-receiving assembly timely according to the fault prediction result, support is provided for realizing predicative maintenance ofthe assembly, and the reliability of the radar system is improved.

Description

technical field [0001] The invention relates to the technical field of radar system fault prediction, in particular to a fault prediction method and a prediction system based on a BP neural network model. Background technique [0002] With the detection requirements of unmanned aerial vehicles, stealth aircraft, ballistic missiles, and new threat targets near space targets, phased array and digital technology have been widely used in radar. In the phased array radar system, the number of digital transceiver components is large and the cost is high. It is the most critical part of the radar system. Whether its performance is normal or not directly affects the performance of the radar system. The digital transceiver component integrates the transmission and reception of electromagnetic wave signals by radar, and has a high failure rate. [0003] When the status parameters of the digital transceiver components are abnormal, the maintenance personnel cannot obtain this informat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 蔡翠翠孟宪猛王本有王梅常志强李石荣
Owner WEST ANHUI UNIV
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