Eccentricity Fault Analysis in Induction Machines
Summary
Induction machines underpin a vast range of industrial and transportation applications, prized for their robustness and efficiency. Eccentricity faults arise when the rotor’s centre deviates from the stator’s axis, producing uneven airgap distributions that introduce torque ripple, vibration and acoustic noise. Such faults are classified as static, dynamic or mixed, depending on whether the displacement remains constant, varies with rotation or combines both behaviours. Detecting and quantifying eccentricity is vital for condition monitoring and predictive maintenance, as unaddressed faults accelerate bearing wear, degrade efficiency and may precipitate catastrophic failure. The analysis exploits characteristic signatures in electrical and mechanical signals: current harmonics, vibration spectra and instantaneous torque oscillations. Techniques range from analytical and finite‐element modelling to advanced signal processing and probabilistic estimation, each balancing accuracy, computational cost and sensitivity to noise. Recent advances have emphasised uncertainty quantification, real‐time diagnostics and digital twin integration, enabling more reliable fault severity assessment and targeted intervention strategies.
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Eccentricity Fault Analysis in Induction Machines publication trend
The graph below shows the total number of articles in eccentricity fault analysis in induction machines across all publications each year (not limited to Nature Index journals).
Technical terms
Airgap: The radial clearance between stator and rotor; its uniformity governs torque production and fault‐induced harmonics.
Static eccentricity: A fixed offset of the rotor shaft from the stator axis, typically arising from assembly tolerances or shaft bending.
Dynamic eccentricity: A rotating eccentricity component that varies with load or speed, often due to bearing wear or shaft imbalance.
Bayesian framework: A statistical paradigm that fuses prior knowledge with observed data to estimate parameters and quantify their uncertainty.
Wavelet packet decomposition (WPD): A multi‐level signal analysis method that partitions frequency bands to reveal fault‐specific components.
Empirical mode decomposition (EMD): An adaptive process that decomposes complex signals into intrinsic mode functions for non‐stationary feature extraction.
Digital twin: A synchronised virtual model of a physical system that mirrors its behaviour in real time for diagnostics and optimisation.
References
- A Bayesian approach to online estimation of airgap spatial variation in induction machines with static eccentricity. Applied Mathematical Modelling (2024).
- Induction Motors Dynamic Eccentricity Fault Diagnosis Based on the Combined Use of WPD and EMD-Simulation Study. Applied Sciences (2018).
- Experimental Identification of a Coupled-Circuit Model for the Digital Twin of a Wound-Rotor Induction Machine. Energies (2024).
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