Balance control module life prediction method and device based on health indexes

A technology of balanced control and health indicators, applied in general control systems, control/regulation systems, test/monitoring control systems, etc., can solve problems such as prediction of remaining service life of difficult systems, aging, etc.

Active Publication Date: 2020-07-10
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the balance control module usually operates in the state of switching between different working conditions, and the collected data is data of multiple components. At this time, the data does not have an obvious aging trend, and it is difficult to predict the remaining service life of the system under multiple working conditions.
Many studies directly use learni

Method used

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  • Balance control module life prediction method and device based on health indexes
  • Balance control module life prediction method and device based on health indexes
  • Balance control module life prediction method and device based on health indexes

Examples

Experimental program
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Effect test

Embodiment 1

[0071] This embodiment discloses a method for predicting the life of a balance control module based on health indicators, including the following steps:

[0072] Step S1: Based on the data of the brake balance control module in each action, extract its eigenvalues ​​at each action;

[0073] Step S2: Construct a health index model, determine the model parameters based on the eigenvalues ​​of the samples of the brake balance control module; and based on the extracted eigenvalues, use the health indicator model after parameter determination to calculate Health indicators at the time;

[0074]Step S3: Construct a life prediction model, based on the health index sequence and the remaining service life sequence of the samples of the brake equalization control module (marking the remaining service life of each sample in each action = the total action of the sample in the entire life cycle Number of times - the number of actions the sample has performed during this action, such as th...

Embodiment 2

[0077] This embodiment is on the basis of embodiment 1, as figure 1 As shown, the feature extraction in the step S1 specifically includes the following steps:

[0078] Step S1.1, extracting static features:

[0079] Based on the driving current curve of each solenoid valve in each action of the brake balance control module, a set of aging characteristics of the solenoid valve is extracted. The aging characteristics of the solenoid valve include the statistical characteristics and energy characteristics of the driving current; the statistical characteristics of the driving current include the response time Δt, local peak value I, local valley value I', stable current value Among them, the energy characteristic is to use the empirical mode decomposition to decompose the driving current curve into Z intrinsic mode function components And the calculated components of each intrinsic mode function energy e z ,Calculated as follows:

[0080]

[0081] Where L is the number ...

Embodiment 3

[0096] This embodiment is based on Embodiment 1. In the step S2, the health index model is aimed at obtaining the health index representing the aging state (system degradation trend) of the balance control module by fusing the accumulated dynamic feature sequence. Considering that the cumulative dynamic feature F consists of each part (F 1 ,F 2 ,F 3 ,F 4 ) are multi-dimensional, let K be the total dimension of the cumulative dynamic feature, that is, the number of features it contains, then the cumulative dynamic feature corresponding to the t-time action of the balance control module can be expressed as F(t)=(f 1 (t), f 2 (t),...,f K (t)), the health index is expressed as a linear combination of features, that is, the constructed health index model is:

[0097] H(t)=w 1 f 1 (t)+w 2 f 2 (t)+...w k f k (t)+...+w K f K (t)

[0098] Among them, H(t) is the health index of the balance control module at the tth action, w 1 ,w 2 ,...,w k ,...,w K Indicates the weig...

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Abstract

The invention discloses a balance control module service life prediction method and device based on health indexes. The method comprises the following steps: S1, calculating the characteristic value of a balance control module during each action based on the data of the balance control module in each action; S2, constructing a health index model, and determining model parameters based on the characteristic values of balance control module samples; based on the extracted characteristic values, calculating health indexes of the balance control module in each action by adopting the health index model after parameter determination; S3, constructing a life prediction model, and training prediction model parameters based on a health index sequence and a residual service life sequence of the balance control module samples to obtain a trained life prediction model; and S4, inputting a historical health index sequence of a balance control module to be detected into the trained life prediction model, and outputting the remaining service life of the balance control module. The service life of the balance control module can be predicted, and the accuracy is high.

Description

technical field [0001] The invention relates to the field of life prediction for a railway locomotive braking system, in particular to a method and device for life prediction of a brake equalization control module based on health indicators. Background technique [0002] With the rapid development of the rail transportation industry, electropneumatic brakes (such as a new generation of electropneumatic brakes - DK-2 braking system) are widely used in railway transportation, and the balance control module is used to achieve balance The closed-loop control of the air cylinder pressure, as a key module to ensure the safe operation of the brake, has received extensive attention from researchers at home and abroad for its life prediction. [0003] The traditional life prediction technology is mainly researched on a single component. Because a single component has a single working condition and the aging trend is more obvious, it is better to predict. However, the balance control...

Claims

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/0283
Inventor 彭军王胜男黄志武杨迎泽李恒蒋富张晓勇刘伟荣程亦君顾欣
Owner CENT SOUTH UNIV
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