2results about How to "Valid conversion" patented technology

The present disclosure relates to a distributed optical fiber sensing data conversion method and device, and a computer device, computer readable storage medium and computer program product, the conversion method comprising: obtaining original data of distributed optical fiber sensing; performing first transformation processing on the original data to transform it from phase response to strain response to obtain first data; performing second transformation processing on the first data to transform it from strain response to displacement response to obtain second data; performing third transformation processing on the second data to transform it from displacement response to velocity response to obtain third data; and outputting the third data. The present disclosure gradually transforms the distributed optical fiber sensing data from phase response to velocity response to finally obtain physically corresponding data, thereby realizing effective conversion of DAS data response. Moreover, the converted physically corresponding data is free of direct current noise, and the effect of the converted data is highly consistent with the actual response.

This invention provides a method for predicting the remaining service life of metallic components based on magnetic coercivity, relating to the fields of materials testing and service life prediction. The method first determines the coercivity values ​​in the original and failed states of the material to establish a coercivity reference range. During the component's service life, multi-point in-situ measurements are performed using pulse magnetization to calculate the current service coercivity value, thereby obtaining a continuous damage severity factor. Then, utilizing the nonlinear relationship between the damage severity factor and the cumulative damage life fraction established through accelerated damage testing, and combining it with component geometric features, stress distribution, and environmental parameters, a service life prediction model is constructed. Finally, the remaining safe service life is calculated based on the model and service history, and a confidence interval is provided. Simultaneously, the test data is stored in a database for model updates. This achieves continuous quantitative characterization of damage states and reliable prediction of remaining service life.