Remaining Useful Life Prediction Through the Derivation of Acceleration Factors Based on Intermittent Inspection Data

ABSTRACT A method is proposed for obtaining critical reliability information throughout the product lifecycle by utilizing intermittent inspection data. The developed methodology is applied to products from industries where intermittent inspection data are accessible. Traditional methods rely on experimental test data to estimate material properties in life‐stress relationships. In contrast, the proposed methodology estimates the parameters of the acceleration model based on the available data. From the case study, an activation energy of 0.76 eV and a humidity index of 1 were derived using product‐related data and an evolutionary. These results are accurately reflective of field conditions. Acceleration factors are calculated using the methodology that considers the degradation differences between normal and field environments. This approach minimizes errors and effectively predicts the remaining useful life (RUL) of the actual product. The proposed methodology provides a systematic analysis process based on actual data. This approach demonstrates significant potential for enhancing product reliability and reducing lifecycle costs. The results of this study demonstrate the ability to achieve realistic and useful predictions of RUL. This supports improved decision‐making for maintenance and replacements.