47results about How to "In line with the actual situation of the project" patented technology

The invention discloses a method for predicting service life of a product by an accelerated degradation testing based on degradation distribution non-stationary time series analysis. The method comprises the following steps of: 1, acquisition and preprocessing of test data; 2, parameter estimation of degradation distribution; 3, time series modeling of degradation distribution parameters; 4, accelerated degradation modeling based on the degradation distribution; and 5, service life prediction based on degradation distribution. The method can macroscopically describe degradation statistical regulation of all samples under accelerated stress, overall analyze the time series of the degradation distribution in an accelerated degradation process, can extrapolate the degradation distribution under the accelerated stress to normal stress to obtain product reliability reflecting the product accelerated degradation random process volatility regulation and service life relationship prediction, improve the credibility on estimation result of service life prediction and reliability, and save more time and is more efficient compared with performance degradation prediction under normal stress level.

The invention provides a high-speed train dynamic performance parameter design optimization method, which relates to the field of parameter design optimization based on high-speed train dynamics simulation analysis, which can effectively use the overall target neural network proxy model to replace the high-speed train dynamics simulation model , combining the design analysis of high-speed trains in the multidisciplinary field with multi-objective optimization algorithms, the analysis and optimization based on the approximate model of high-speed train dynamics simulation are carried out. The specific steps of the method are as follows: establish a high-speed train multi-rigid body dynamics simulation model, determine the important input and output design space involved, select a sampling strategy to obtain a suitable design space sample set for high-speed train dynamic performance analysis; use Bayesian regularization The method improves the generalization accuracy of the overall objective neural network, and constructs an overall objective neural network proxy model with error control within a certain range by adjusting the number of nodes in the hidden layer; using the improved overall objective neural network proxy model, combined with differential evolution intelligence The multi-objective optimization algorithm is used to obtain the optimized high-speed train design parameters. It is mainly used for dynamic analysis and design optimization of high-speed trains.

The invention relates to a fault diagnosis system sensor optimal configuration method based on a quantitative structure model. The method comprises the following steps: 1) establishing a structure model of a fault diagnosis system through a structure analysis method, wherein the structure model is represented by two matrixes, that is an analytical redundant relation matrix and a fault feature matrix, and the structure model can qualitatively describe the fault diagnosis system; 2) detecting nondeterminacy with the structure model of the fault diagnosis system and sensors being combined to obtain quantitative description of the fault diagnosis system and realize the purpose of quantitatively analyzing the fault diagnosis system, and establishing a calculation formula for fault diagnosis system key performance indicators with the detection of nondeterminacy being taken into consideration; 3) establishing a sensor optimization model of the fault diagnosis system; and 4) solving the optimization model through a genetic algorithm. The sensor optimal configuration method for the fault diagnosis system can effectively save the cost of the sensors of the fault diagnosis system, and the configuration result accords with engineering practice situations better.