Method for predicting the performance degradation trend of an electronic device

A technology of electronic devices and prediction methods, applied in the fields of instruments, electrical digital data processing, special data processing applications, etc., can solve the problems that the performance degradation trend cannot be well reflected, and the prediction effect is reduced.

Active Publication Date: 2019-12-31
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

When the device is degraded, the prediction result will be affected by the device health status information and early degradation information, so the prediction result cannot reflect the current performance degradation trend well
At the same time, since the online learning machine uses the least mean square criterion as the training basis of the output layer, its prediction model is easily affected by non-Gaussian noise and singular values, resulting in reduced prediction effect

Method used

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  • Method for predicting the performance degradation trend of an electronic device
  • Method for predicting the performance degradation trend of an electronic device
  • Method for predicting the performance degradation trend of an electronic device

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Embodiment

[0071] figure 1 It is a flowchart of a method for predicting the performance degradation trend of electronic devices of the present invention.

[0072] In this example, if figure 1 Shown, a kind of prediction method of electronic device performance degradation tendency of the present invention comprises the following steps:

[0073] S1. Offline establishment of the prediction model of the initial correlation entropy extreme learning machine

[0074] S1.1. Obtain the historical data of the electronic device under test{x i ,y i}, where, i=1,2,...,k-1, k-1 represents the number of historical data before real-time prediction, x i is the i-th real-time data of the electronic device under test, y i for x i The corresponding expected degradation trend characteristics;

[0075] S1.2. Randomly generate the hidden layer weight {w of the correlation entropy extreme learning machine 1 ,w 2 ,...,w M} and bias {b 1 ,b 2 ,...,b m}, where M represents the dimension of the hidden ...

example

[0127] In order to illustrate the technical effect of the present invention, the real-time flow prediction of the direct current transmission ratio of a photocoupler is now used as an example to verify the present invention.

[0128] A photocoupler is an electronic component that uses light as a medium to transmit electrical signals and converts electrical energy to optical energy, and is used to isolate input and output electrical signals. The DC current transfer ratio of an optocoupler can effectively reflect the health status of the device. In order to verify the effectiveness of the present invention, a prediction model is established through the method of the present invention to predict the trend of the real-time streaming data in the degraded state of the optocoupler.

[0129] Simultaneously, the method described in the present invention and relevant entropy extreme learning machine (RCC-ELM), online extreme learning machine (MOS-ELM), M estimates online extreme learnin...

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Abstract

The invention discloses a method for predicting the performance degradation trend of an electronic device. A prediction model of an initial correlation entropy extreme learning machine is establishedoffline; the dynamic correlation entropy extreme learning machine is updated on line, a codebook mechanism combined with singular values is constructed, and then the singular values in historical dataare identified, so that the influence of noise and the singular values in the data on a prediction model is overcome, and the final prediction effect of the prediction model is improved.

Description

technical field [0001] The invention belongs to the technical field of reliability of sub-components, and more specifically, relates to a method for predicting the performance degradation trend of electronic devices. Background technique [0002] With the rapid development of big data, cloud computing and industrial Internet, the intelligent fault diagnosis and health management of electronic devices has attracted much attention in recent years due to the advantages of sharing resources and related services. At the same time, with the development of intelligent health management, the speed of information interaction between electronic devices and health management platforms has become faster and the scale of information has become larger. In this regard, the acquisition of equipment big data helps to improve the accuracy of predictions. On the other hand, the performance degradation trend data of electronic equipment belongs to the real-time stream, which has the characteri...

Claims

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Application Information

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IPC IPC(8): G06F17/50
Inventor 刘震梅文娟杜立程玉华黄建国白利兵
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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