Information system for current state diagnostics of induction motors

Abstract

Purpose. The paper addresses the urgent scientific and technical problem of non-contact real-time diagnostics and condition monitoring of industrial induction motors without disrupting continuous technological processes. The primary objective is to develop and evaluate a mobile information-diagnostic system capable of assessing the current operational state and predicting the remaining useful life (RUL) of induction motors. Method. The proposed methodology utilizes measured stator currents to evaluate the parametric electromagnetic asymmetry of stator phases. Functional analysis and system decomposition were performed in accordance with the IDEF0 modeling standard. The software architecture was developed using a client-server paradigm, implementing asynchronous background data processing via Kotlin coroutines, REST API network interaction over HTTPS, and centralized data management based on a distributed MySQL database. Results. A mobile diagnostic application operating on Android OS was developed and integrated as a client node in a distributed diagnostic environment. Industrial field testing was conducted at PrJSC «Poltava Mining and Processing Plant» on a 320 kW AKZ-12-39-6UKHL4 induction motor driving a heavy-duty belt conveyor. The system successfully detected low-frequency acoustic vibrations (0.5–1 Hz) and current amplitude oscillations (5–10 %), identifying their root cause as contact degradation in the rotor rheostat. The application calculated the motor’s RUL trend with an average coefficient of 0.908. Scientific novelty. The scientific novelty lies in the advancement of diagnostic system design principles through the synergy of stator current asymmetry identification algorithms, parallel thread execution, real-time data visualization, and automated client-server database synchronization. Practical importance. The implementation enables early fault detection, reduces unscheduled downtime, lowers maintenance expenditures, and supports predictive maintenance strategies for fleets of industrial electrical machinery.