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

Abstract

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 development prospects of such systems were drawn. The article presents a general concept of a comprehensive reliability management system for industrial equipment. The proposed approach is grounded in the application of predictive analytics, machine learning, probabilistic-physics degradation 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 degradation 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 process. To implement this, the development of a unified predictive system for reliability management 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 data. The study also highlights the practical relevance of the proposed technology for critical infrastructure. The system supports localized data processing, reducing the need for cloud infrastructure and minimizing deployment costs.