Industry 4.0 and Condition Control of Induction Machines Based on Electric and Mechanical Properties

This paper presents a theoretical study of a Condition Monitoring Method (CMM) for three-phase induction machines within the framework of Industry 4.0. The proposed approach integrates electrical and mechanical indicators to enhance fault detection sensitivity and reliability in digitally connected industrial systems. Unlike conventional Machine Current Signature Analysis (MCSA), the CMM employs a model-based structure supported by adaptive learning, intelligent alarm logic, and built-in redundancy. These features enable the system to interpret complex signal patterns, distinguish transient disturbances from persistent faults, and avoid missed alarms caused by learning an already degraded state. The innovation of this work lies in the formulation of a unified theoretical framework that connects model-based residual analysis, adaptive statistical decision-making, and digital system integration. The study contributes to knowledge by defining how these elements can be architected within Industry 4.0 environments, enabling predictive maintenance strategies that are data-driven, interoperable, and self-adaptive. Although no experimental validation is presented, the conceptual framework establishes a foundation for the future development of intelligent diagnostic systems that combine physical modelling with digital analytics. In summary, this work provides a novel conceptual model that bridges traditional condition monitoring and modern cyber-physical production systems, contributing to the theoretical advancement of intelligent, Industry 4.0-compatible maintenance architectures.