Trends in the Implementation of Artificial Intelligence for the Diagnosis and Prevention of Emergency Situations

Citation: Oleksandr Balaniuk, "Trends in the Implementation of Artificial Intelligence for the Diagnosis and Prevention of Emergency Situations", Universal Library of Engineering Technology, Volume 01, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article provides an analysis of the systemic causes of the low economic return and limited accuracy of artificial intelligence (AI) systems when diagnosing emergency modes in the heavy industry segment. The aim of the work is to empirically substantiate the need to shift from the Data Filtering paradigm (algorithmic cleansing) to the Data Readiness concept (engineering preparation of assets). The methodological basis includes a systematic literature review and a four-year longitudinal case study of spiral classifiers modernization. Within the framework of the study, the transition from mechanically unstable open gear transmissions to IP66-rated direct drives equipped with torque arms was implemented. It is demonstrated that this engineering intervention eliminated the root cause of mechanical noise, reducing drive vibration from 7.8 mm/s to values below 2.0 mm/s. This mechanical stabilization allowed for the reliable identification of weak defect signatures (0.3 mm/s) that were previously masked by noise, thereby significantly increasing diagnostic accuracy. The study argues that implementing the Data Readiness strategy enables the transition from reactive anomaly detection to full-scale Prognostics and Health Management (PHM). The key results indicate that this approach creates a verified foundation for high-accuracy hybrid digital twins, leading to a reduction in unplanned downtime and the prevention of complex cascading failures. The conclusion is that the effectiveness of AI in heavy industry is achieved through the symbiosis of deep mechanical modernization and advanced data analytics. The presented results and approaches are oriented toward reliability engineers, industrial enterprise management, and data science specialists involved in the implementation of Industry 4.0 technological solutions.

Keywords: Artificial Intelligence, Data Readiness, Data Filtering, Prognostics and Health Management (PHM), Remaining Useful Life (RUL), Digital Twin, Mechanical Noise, Heavy Industry, Spiral Classifier, Predictive Maintenance (PdM).

https://doi.org/10.70315/uloap.ulete.2024.0101007