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Product Life Prediction Method Based on Degradation Data Preprocessing Based on Wavelet Analysis

A technology of degradation data and product life, applied in the field of data processing, it can solve the problems of difficult life prediction results and difficult product degradation process, etc., to achieve the effect of improving accuracy

Inactive Publication Date: 2011-11-30
BEIHANG UNIV
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

In the case of such degradation and temperature drift superposition, the obtained performance parameter degradation data is difficult to truly reflect the degradation process of the product
Therefore, when actually observing, analyzing, and using these data to predict product life, the life prediction results obtained through it are difficult to accurately and truly reflect the actual situation of the product

Method used

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  • Product Life Prediction Method Based on Degradation Data Preprocessing Based on Wavelet Analysis
  • Product Life Prediction Method Based on Degradation Data Preprocessing Based on Wavelet Analysis
  • Product Life Prediction Method Based on Degradation Data Preprocessing Based on Wavelet Analysis

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Experimental program
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Embodiment

[0052] If the temperature stress accelerated degradation test is performed on a product, the sample size is 1, the temperature stress is 100°C, and the test time is 1000 hours. The degradation data is now preprocessed.

[0053] Step 1: Collect degradation and temperature data

[0054] For the degraded data to be processed y p At the same time, the temperature sensor is used to collect the product temperature data T corresponding to the degradation data sampling point. The collected data such as figure 2 , image 3 Shown.

[0055] Step 2: Extract the degradation trend item

[0056] Use the multi-resolution analysis method in wavelet analysis (wavelet analysis toolbox in matlab) to decompose the degraded data, and decompose the degraded data y p Decomposed into approximate degradation trend term y t ′ And the noise term y n . When the resulting noise term y n Correlation with temperature data T r 1 When it reaches the maximum value of 0.9416 (the correlation value range is (0, 1)), t...

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Abstract

The invention discloses a product service life prediction method for pre-processing degradation data based on wavelet analysis. The method comprises the following steps of: 1, collecting degradation and temperature data; 2, extracting a degradation trend item; 3, decomposing a noise item; 4, reconfiguring the degradation data; and 5, predicting the service life of a product. By the method, the interference of temperature fluctuation in the degradation data can be removed, the real degradation data can be restored; the method can be used for accelerating the pre-processing of the degradation data in an accelerated degradation test, so that the accuracy of prediction on the service life of the product is improved; and the method can be used for directly extracting performance parameter fluctuation data under the action of temperature fluctuation and temperature drift, so that the steps of building a model by adopting temperature and the like can be eliminated.

Description

Technical field [0001] The invention is a product life prediction method based on wavelet analysis of degradation data preprocessing, and belongs to the technical field of data processing. Background technique [0002] For electronics, optoelectronics and other products, temperature is their sensitive stress and affects their performance parameters. In actual use and testing, these performance parameters will not only show a tendency of degradation over time, but also show at the same time. The change trend related to temperature (temperature drift of performance parameters). In the case of such degradation and temperature drift superimposition, the performance parameter degradation data obtained cannot truly reflect the degradation process of the product. Therefore, in actual observation, analysis and use of these data to predict the life of the product, the life prediction results obtained through it are difficult to accurately and truly reflect the actual situation of the pro...

Claims

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Application Information

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IPC IPC(8): G06F17/50
Inventor 王立志李晓阳姜同敏庄晓天
Owner BEIHANG UNIV
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