Determining residual product life considering multiple degradation processes and variable loading.

Abstract

Abstract. As of today, significant progress has been observed in the field related to the analysis of various types of degradation processes occurring in technical elements and systems. In most studies, each of these processes is examined individually, and the comprehensive assessment of the impact of all degradation processes on the final residual life value is limited by considering a single dominant process — the one that develops most intensively, known in reliability theory as «the weakest link». Applying this method simplifies the overall degradation profile significantly and contributes to an overestimation in the final prediction which is unacceptable in critical application systems. The article proposes an approach that, in an accessible engineering form, allows for the necessary calculations to assess the residual life of a product that is simultaneously under the influence of multiple degradation processes with varying degrees of participation and temperature loads. This approach relies on calculating the mean rate and coefficient of variation for the generalized degradation process. The calculations are conducted employing a probabilistic-physical approach, within which lies a probability model in the form of a diffusion monotonic failure distribution (DM-distribution). This distribution is presented with the involvement of a Markov diffusion-type random process with a constant speed which properly adapts and aligns statistical failure data of mechanical objects. Being influenced by multiple degradation processes, the normalization of the initial data and information regarding the proportions of each of the components in the general degradation process has been used for the first time. The application of this approach allows for a more accurate estimation of the product’s residual life compared to the estimation obtained by considering only one dominant degradation process. The detailed assessment of the residual life, which is obtained after refinements, contributes to reducing operational costs through the optimization of technical maintenance intervals and establishing the actual service life of the researched objects.