Condition Monitoring and Fault Diagnosis in Induction Motor Systems

Induction motors underpin modern industry and infrastructure, converting electrical energy into mechanical motion across applications from manufacturing assembly lines to renewable energy systems. Condition monitoring and fault diagnosis of these motors are pivotal for preemptive maintenance, safeguarding operational continuity and reducing lifecycle costs. Traditional diagnostic approaches rely on analysis of electrical current and vibration signals to reveal fault-specific frequency components. Advances in signal processing—in particular time–frequency techniques such as wavelet transforms and the Hilbert–Huang Transform—have enhanced the detection of subtle transient anomalies. Concurrently, machine learning and deep learning models, including support vector machines and convolutional neural networks, have automated feature extraction and improved classification accuracy. The fusion of multi-sensor data streams—encompassing current, vibration, thermal and magnetic flux measurements—now enables holistic evaluation of rotor, stator and bearing health. Together, these developments facilitate more reliable, real-time assessments, contributing to energy efficiency, reduced unplanned downtime and extended equipment lifespan.

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Condition Monitoring and Fault Diagnosis in Induction Motor Systems publication trend

The graph below shows the total number of articles in condition monitoring and fault diagnosis in induction motor systems across all publications each year (not limited to Nature Index journals).

Technical terms

Condition monitoring: Ongoing measurement and analysis of system parameters to assess equipment health.

Fault diagnosis: Identification and classification of abnormal conditions within machinery.

Motor Current Signature Analysis (MCSA): Technique analysing electrical current spectra to detect characteristic fault frequencies.

Hilbert–Huang Transform (HHT): Time–frequency analysis method decomposing signals into intrinsic mode functions for transient feature extraction.

Support Vector Machine (SVM): Supervised machine learning algorithm that classifies data by finding optimal separating hyperplanes.

Convolutional Neural Network (CNN): Deep learning architecture effective at automatic feature extraction from raw signal or image data.

References

1. Hilbert-Huang Transform and machine learning based electromechanical analysis of induction machine under power quality disturbances. Results in Engineering (2024).
2. Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor. Energies (2020).
3. Fusion of Vibration and Current Signatures for the Fault Diagnosis of Induction Machines. Shock and Vibration (2019).
4. State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors. Energies (2017).