Fault detection and diagnosis in three phase IM using DSP: hardware implementation and real-time approach

FPGA – EMBEDDED FUZZY LOGIC-PROCESSOR FOR ONLINE DETECTION OF BROKEN BARS ON INDUCTION MOTORS

Preliminary results show that the fuzzy logic-based processor described herein can be used for accurately detecting broken bars in induction motors and can be used for accurately detecting broken bars in induction motors.

Smart Motor Protection and Phase Monitoring System for Three-Phase Motors Using Arduino and IoT Integration

Experimental validation demonstrates reliable phase detection, rapid relay response, and effective remote alerting, confirming the system's suitability for industrial automation, motor protection, and smart energy management applications.

An Investigation of Fault Signatures in Online Condition Monitoring Methods for Shaft Misalignment and Mass Unbalance Fault Diagnosis of PMSM Driven Systems

Mechanical faults in permanent magnet synchronous motor (PMSM)-driven systems can introduce disturbances and system interruptions leading to reduced performance and reliability. Effective fault diagnosis is essential for early fault detection and identification, which enables timely maintenance, reduced downtime, and efficient operation of the system. This article presents an investigation of fault signatures in online condition monitoring methods, focusing on mechanical fault diagnosis of PMSM driven systems. Angular shaft misalignment and mass unbalance faults are investigated under two different severity levels. Three axis vibration, acoustic emission, stator currents, axial and radial stray flux, and shaft torque are evaluated for fault detection, severity assessment and fault discrimination. In the first place, a theoretical framework describing the influence of the specific mechanical faults on the mechanical and electromagnetic behavior of the PMSM is established, enabling the identification of characteristic fault-related signatures. An experimental test bench is developed, integrating a PMSM with vibration, current, stray flux, acoustic emission, and torque sensors. Fault signatures are analyzed in both nominal operating condition and variable speed and load levels. Moreover, load and speed transients are investigated. Finally, to extract the most informative sensors, a data driven approach with mutual information, random forest, and Shapley additive explanations is employed. Results from this experimental investigation highlight the tradeoffs between diagnostic performance, practical implementation, and the effectiveness of combining domain knowledge with data-driven approaches for accurate, early, and cost-effective PMSM fault diagnosis.

Control and Monitoring System for a Three-Phase Induction Motor Based on Arduino Mega and VSD Integrated with LabVIEW

The novelty of this research lies in the integration of a responsive, cost-effective, multi-parameter control and monitoring platform equipped with automatic data logging within a single integrated HMI interface, which is ready to be applied for research as well as industrial automation laboratory practices.

Real-Time Monitoring and Fault Detection System for Solar Photovoltaic Installations

A new diagnostic method based on support vector machine for short circuit winding faults in induction motors

The outcomes prove that the integrated technical framework (time-domain features + FR + SVM) provides zero false alarms and a balanced diagnostic system that combines computational speed with high precision.