A conceptual framework for a comprehensive industrial equipment reliability management system using predictive analytics

An analysis of existing solutions was conducted to support the unification of a newly developed industrial equipment reliability management system. Based on a review of the advantages and disadvantages of current predictive analytics platforms, conclusions regarding the future devel-opment prospects of such systems were drawn. The article presents a general concept of a com-prehensive reliability management system for industrial equipment. The proposed approach is grounded in the application of predictive analytics, machine learning, probabilistic-physics deg-radation models, and IoT component integration into a digital control infrastructure. The system enables failure prediction, RUL estimation, and automated decision-making for maintenance scheduling. The paper details the scientific and technical implementation aspects, including the deployment of mathematical models, principles of anomaly detection, and risk evaluation. For local data storage, a data architecture is proposed that is optimized for stream processing and combines storage for both structured and semi-structured data. The core idea lies in a hybrid methodology, combining neural network-based modeling with physical studies of material deg-radation and component failure statistics to accurately assess reliability, forecast remaining useful life, and determine regulated operational lifetimes. A probabilistic-physics approach is proposed, employing advanced failure models with physically interpretable parameters such as the mean degradation rate and the coefficient of variation of the generalized degradation pro-cess. To implement this, the development of a unified predictive system for reliability man-agement is proposed. The identification of threshold patterns — parameter values or system states that define the resource limits — is assigned to ML/AI models trained on operational da-ta. The study also highlights the practical relevance of the proposed technology for critical in-frastructure. The system supports localized data processing, reducing the need for cloud infra-structure and minimizing deployment costs.