Product residual service life prediction method considering individual difference and measurement errors
A technology for measurement error and life prediction, applied in data processing applications, complex mathematical operations, resources, etc., can solve problems such as inaccurate prediction, remaining service life of product degradation modeling, ignoring individual differences, etc., and achieve the goal of improving accuracy Effect
Active Publication Date: 2020-07-28
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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The invention discloses a product residual service life prediction method considering individual difference and measurement errors. The method comprises the following steps of 1, obtaining a group ofperformance degradation data capable of representing a product degradation condition; 2, taking the real degradation amount of the product as a hidden variable, and establishing a probability densityfunction of the hidden variable by an inverse Gaussian process so as to describe statistical characteristics of real degradation; 3, establishing an inverse Gaussian process model considering individual difference; 4, establishing a probability density function of the measurement error; 5, establishing an inverse Gaussian process model considering both individual difference and measurement error;6, according to the product degradation data obtained in the step 1, establishing a likelihood function for an inverse Gaussian process model considering individual difference and measurement errors,and simplifying the inverse Gaussian process model by utilizing Monte Carlo integration; 7, solving the simplified likelihood function in the step 6 by using an EM algorithm to obtain the maximum estimated value of the unknown parameter; and step 8, predicting the residual service life of the product by using the parameter estimation result in the step 7.
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