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Method for predicting remaining life of LED driving power of railway vehicles

A technology for LED driving and rail vehicles, which is used in electrical digital data processing, special data processing applications, instruments, etc. It can solve problems such as poor prediction effect, poor feasibility, and difficulty in obtaining failure data, so as to improve accuracy and reduce uncertainty. Effect

Active Publication Date: 2016-04-06
CHANGCHUN UNIV OF TECH +1
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AI Technical Summary

Problems solved by technology

[0003] At present, the remaining life prediction of rail vehicle LED drive power supply adopts the artificial intelligence method based on failure data. Although the artificial intelligence method has a high degree of data fitting, its prediction effect on the future is poor, and for high reliability products , the failure data is often difficult to obtain in a short period of time, so its feasibility is poor

Method used

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  • Method for predicting remaining life of LED driving power of railway vehicles
  • Method for predicting remaining life of LED driving power of railway vehicles
  • Method for predicting remaining life of LED driving power of railway vehicles

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Experimental program
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Embodiment Construction

[0029] Such as figure 1 As shown, the specific implementation method adopts the following steps:

[0030] (1) Based on the Wiener process, the degradation model of the rail vehicle LED drive power supply is established.

[0031] (2) Use the Hallberg-Peck acceleration model to construct the relationship between the drift coefficient θ and the temperature and humidity stress in the degradation model, and the relationship between the coefficient θ and the temperature and humidity stress.

[0032] (3) Using the uninformative prior distribution, using the Bayes method, by integrating the joint posterior distribution to remove redundant parameters, the parameters in the degradation model are updated to obtain

[0033] Get its posterior distribution. According to Bayes theorem, the posterior distribution can be expressed as:

[0034] p(θ|y)∝f(y|θ)p(θ)(1)

[0035] In the formula, p(θ|y) is the probability density function of the posterior distribution, f(y|θ) is the likelihood fun...

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Abstract

The invention discloses a method for predicting remaining life of a LED driving power of railway vehicles, belonging to the technical field of reliability engineering. The method comprises the following steps of: 1, establishing a degradation model of the LED driving power of railway vehicles based on a Wiener process; 2, constructing a relation between a drift coefficient Theta and a temperature and humidity stress in the degradation model through a Hallberg-peck accelerated model; 3, using non-informative prior distribution, updating parameters in the degradation model through a Bayes method, and thereby obtaining posteriori distribution thereof; 4, using temperature and humidity as accelerated stress, acquiring performance degradation data of the LED driving power of railway vehicles in real time, putting the data in a Bayes algorithm, and extrapolating the reliability and the remaining life of the LED driving power of railway vehicles under normal stress condition. The method provided by the invention has the advantages of being capable of improving precision of prediction of the remaining life of the LED driving power of railway vehicles, and reducing uncertainty of prediction.

Description

Technical field: [0001] The invention relates to a method for predicting the remaining life, in particular to a method for predicting the remaining life of a rail vehicle LED drive power supply. It belongs to the technical field of reliability engineering. Background technique: [0002] The existing remaining life prediction methods can be divided into two categories: model-based prediction methods and data-based prediction methods. With the development of related technologies such as signal acquisition and signal processing, it is often possible to collect a wealth of system operation data, and establish a corresponding mathematical model based on these data, which is a data-based prediction method. This method has gradually become the mainstay of the prediction method. The mainstay, data-based forecasting methods are mainly composed of artificial intelligence and probability statistics. [0003] At present, the remaining life prediction of rail vehicle LED drive power su...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/00
Inventor 张邦成陈珉珉李波高智尹晓静
Owner CHANGCHUN UNIV OF TECH
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