Degradation Data Self-Analysis layer for integrated maintenance activities

Reliability-oriented approach based on Monte Carlo simulations is a well-established methodology for coordinating maintenance activities of any technical system. Usually, coordination is conducted using holistic performance indicators, which are obtained from the convolution between the stochastic system availability and the system service required in a time horizon of t. Specifically, the system stochastic availability modeling is composed of the degradation process due to the system operation and the planning of the maintenance activities needed to keep the system operating at the desired standards. In the case of the degradation modeling process, given its random nature, it is addressed with predictions, which in practice, consist of generating random samples of the stochastic degradation processes from probability distributions, and the parameterization is usually estimated by fitting the distributions to historical degradation data for each technical component considered. Crucial to forecasting accurate performance indicators is the use of up-to-date information, i.e., the self-update of historical degradation data. In this paper, to address accurate performance indicators, we propose using the machine learning approach to update the adaptable model layers affected by changes in the degradation data. The paper's case study is an overhead crane system of a hot rolling mill process in a steel plant, which operates under hazardous conditions and continuously. We focus on overhead cranes because they are critical components of production processes. The paper's subject is validating the performance of a self-analysis layer, which processes the degradation data of the analyzed technical devices. The engineering solution ensures well-processed inputs for the problem of coordination of maintenance activities of overhead cranes, which is the object of the study of this research.

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1. Pełne imiona podano na stronie internetowej czasopisma w "Authors in other databases."

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2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).