Digitalization Tools for Equipment Predictive Maintenance as an Industrial Safety Factor of Metallurgical Industry Enterprises
Abstract
According to data from Rostechnadzor, a high level of wear and tear on the fixed assets of metallurgical enterprises is one of the main causes of accident and industrial injury rates at hazardous production facilities. The traditional system of scheduled preventive maintenance does not allow for predicting sudden equipment failures that cause severe accidents: ruptures of gas ducts, collapses of overhead cranes, destruction of the lining of metallurgical furnaces, and explosions of air-gas mixtures. The goal of the study is the systematization of digital tools of equipment predictive maintenance and the substantiation of their impact on the industrial safety of metallurgical enterprises. The evolution of equipment maintenance systems, regulatory requirements of the Federal Law of 21.07.1997 № 116-FZ “On industrial safety of hazardous production facilities”, and the Federal norms and rules “Safety rules of processes of metals production or use” have been considered. A classification of predictive maintenance digital tools is proposed: vibrational diagnostics, thermography, lubricant analysis, digital twins, machine learning based on failure history data, and integrated APM/EAM systems. A matrix of equipment prioritization based on failure criteria, “failure hazard”, “failure frequency”, and “digitalization potential” has been developed. A system of implementation efficiency evaluation indices has been proposed, including technical, safety, and economic indices. The scientific innovation is the systematization of predictive maintenance tools specifically for the safety of the metallurgical industry, rather than solely for economic efficiency. The practical significance lies in the potential use of the results by industrial safety services and chief engineers to substantiate investments in predictive diagnostics of critical equipment at hazardous metallurgical production facilities.