Life prediction method fusing field data and two-stage accelerated degradation data

Abstract

The invention discloses a life prediction method fusing field data and two-stage accelerated degradation data, and the method comprises the steps: determining a transformation point position of a degradation process according to the accelerated degradation data of a motor insulation material test sample under different thermal stresses through SIC and a residual sum of squares minimum criterion; constructing a motor insulation performance degradation model based on a two-stage random process under the action of thermal stress, obtaining a relationship between a model parameter and a change point distribution parameter and a temperature according to an Arrhenius equation, and extrapolating a probability density function and a model parameter of a change point position of a product in a degradation process at a normal temperature; utilizing on-site degradation data of a to-be-tested motor insulating material, using a CUSUM control chart for identifying a change point, and determining the probability of occurrence of the change point in combination with a probability density function of the change point; and taking the accelerated degradation data as prior information of model parameters, obtaining a posterior estimation value of the model parameters based on a Bayes method according to the field degradation data, obtaining a cumulative distribution function of product life under the sense of first arrival time, and realizing real-time life prediction of motor insulation.

Description

technical field [0001] The invention relates to the technical field of equipment life prediction, in particular to a life prediction method that combines on-site data and two-stage accelerated degradation data. Background technique [0002] In order to cope with energy shortage and air pollution, my country attaches great importance to the development of new energy power generation and electric vehicles. As an important equipment for new energy power generation, wind turbines operate under complex and harsh environmental conditions for a long time. Generator insulation damage is one of the important reasons for wind turbine failures. Due to the operating characteristics of electric vehicle drive motors such as frequent starting, variable speed, and changing road conditions, the failure of the drive motor is mainly caused by damage to the motor windings, and one of the important reasons for winding damage is insulation damage. Therefore, it is of great significance to predic...

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Problems solved by technology

[0006] In view of this, the present invention provides a life prediction method that combines field data and two-stage accelerated degradation data, which solves the problem of the lack of historical failure data, difficulty in collection, and the multi-stage degradation process that does not consider the change point in the modeling of accelerated degradation data. The problem of inaccuracy in reliability assessment

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