Reliability Assessment And Life Prediction Based On Uncertainty Theory

The importance of reliability assessment and life prediction is reflected in all stages of product design,development,production,and use.To address the problem of low accuracy of reliability assessment and life prediction due to the cognitive uncertainty caused by small sample size and degradation data for products with high reliability and long life,we conduct a research on reliability assessment and life prediction based on accelerated degradation experiments based on uncertainty theory to provide theoretical support for reliability assessment of products with small sample size and degradation data.The study will provide theoretical support for product reliability assessment with small sample size and little degradation data.Degradation modeling based on uncertainty theory.In view of the cognitive uncertainty of product performance degradation caused by small sample size and little degradation data,we establish degradation modeling based on uncertainty theory,analyze the common parameter estimation methods of uncertain degradation models,compare the advantages and disadvantages of different parameter estimation methods through simulation,and determine the parameter estimation methods.Accelerated degradation reliability assessment based on Liu process.To address the cognitive uncertainties of degradation initial value,measurement error,degradation rate and random fluctuations caused by small sample size and degradation data in the reliability assessment based on accelerated degradation test,an accelerated degradation reliability assessment model based on the Liu process is established considering multiple cognitive uncertainties at the same time.A two-step parameter estimation method based on α-path method and least-squares method is proposed to verify the validity of the proposed model and parameter estimation method through avionics connector and Ga As laser performance degradation examples,and to give reliability assessment and parameter sensitivity analysis.Accelerated degradation reliability assessment based on uncertain differential equations.An accelerated degradation model based on uncertain differential equations is developed considering both the degradation rate and the cognitive uncertainty of random fluctuations.A two-step parameter estimation method based on α-path method and generalized moment estimation is proposed,and the validity of the proposed model and parameter estimation method is verified by numerical simulations and electrical connector degradation examples.Life prediction based on the Liu process.A nonlinear life prediction model considering the cognitive uncertainty of degradation rate is developed based on the Liu process,and the model parameters are updated based on the similarity of the product historical degradation data and field degradation data based on the least squares method,and the validity of the proposed model and the proposed method are verified based on the NASA battery data set.