A Method for Predicting the Remaining Service Life of Products Considering Individual Differences and Measurement Errors
A measurement error and life prediction technology, applied in data processing applications, instruments, design optimization/simulation, etc., can solve problems such as inaccurate prediction, remaining service life of product degradation modeling, ignoring individual differences, etc., to improve accuracy Effect
Active Publication Date: 2022-06-28
BEIHANG UNIV
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[0004] The invention provides a method for predicting the remaining service life of a product considering individual differences and measurement errors. It is more in line with the actual engineering situation
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[0047] The following will combine figure 1 And a certain type of laser performance degradation case, the present invention will be further described in detail.
[0048] See figure 1 As shown, it is a schematic flow chart of the implementation steps of the method of the present invention, and the remaining service life is predicted for the case of laser performance degradation.
[0049] Step 1: GaAs lasers are widely used in laser printing, optical communication systems, military and other fields. A set of performance degradation data of a certain type of GaAs laser is obtained from the literature, which is the percentage data of the change of operating current of a certain type of GaAs laser with time obtained through degradation tests at 80°C. The product fails when the increased current exceeds 8% of the initial current. use Y 0：k {y 0 ,y 1 ,...,y k} means at time 0=t 0 1 k The degradation data obtained during the measurement, the detailed data are shown in the follo...
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A method for predicting the remaining service life of a product considering individual differences and measurement errors. The steps are as follows: 1. Obtain a set of performance degradation data that can characterize the degradation of the product; The process establishes its probability density function to describe the statistical characteristics of real degradation; 3. Establish an inverse Gaussian process model considering individual differences; 4. Establish the probability density function of measurement errors; 5. Establish both individual differences and measurement errors The inverse Gaussian process model; 6. According to the product degradation data obtained in step 1, the likelihood function is established for the inverse Gaussian process model considering individual differences and measurement errors, and it is simplified by Monte Carlo integral; 7. Use the EM algorithm to solve the simplified likelihood function in step 6 to obtain the maximum estimated value of the unknown parameter; step 8: use the parameter estimation results in step 7 to predict the remaining service life of the product.
Description
[0001] Technical field [0002] The invention provides a method for predicting the remaining service life of a product considering individual differences and measurement errors, which is suitable for performing corresponding degradation modeling and remaining service life prediction on products with degradation characteristics. The uncertainty of the error can effectively improve the accuracy of the remaining service life prediction. The invention belongs to the field of reliability and system engineering. Background technique [0003] Generally speaking, the same batch of products often has differences among different individuals due to the manufacturing process, and the performance of each product will also vary under different operating environments, thus showing different degradation trajectories, that is, individual differences . In addition, in engineering applications, imperfect measuring instruments or fluctuations in the measuring environment will affect the collect...
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IPC IPC(8): G06F30/20G06F119/04G06F17/18G06Q10/06
CPCG06F17/18G06Q10/0639
Inventor 孙博李豫王自力冯强任羿杨德真
Owner BEIHANG UNIV