Interturn Fault Diagnosis in Permanent Magnet Synchronous Motors

Permanent Magnet Synchronous Motors (PMSMs) are widely adopted in electric vehicles, renewable energy systems and industrial drives owing to their high efficiency, compact form factor and favourable torque characteristics. However, stator interturn short circuits—where one or more turns within a single phase winding become electrically bridged—pose a critical threat to motor reliability and performance. Such faults introduce asymmetries in the magnetic field, generate additional losses and may lead to catastrophic demagnetisation or drive instability if not identified at an incipient stage. Diagnosis of interturn faults typically relies on either signal-based approaches, which analyse features extracted from current, voltage or acoustic measurements, or model-based schemes that exploit analytical redundancy and residual generation to detect deviations from an expected electrical model. Recent advances have explored time–frequency decomposition, Hilbert transform and high-frequency impedance methods to extract subtle fault signatures, while statistical and machine-learning algorithms, including convolutional neural networks, have been applied to improve classification accuracy and severity estimation. Model-based techniques leverage structural analysis of the motor’s dynamic equations to create residuals sensitive to fault terms, enabling real-time detection of even a single turn short in a multi-turn winding. The convergence of data-driven and physics-based methods is enhancing diagnostic robustness across varying load and speed conditions, and reducing dependency on additional sensors. Early fault detection not only extends motor service life but also optimises maintenance schedules and improves energy efficiency in large-scale applications.

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Interturn Fault Diagnosis in Permanent Magnet Synchronous Motors publication trend

The graph below shows the total number of articles in interturn fault diagnosis in permanent magnet synchronous motors across all publications each year (not limited to Nature Index journals).

Technical terms

Interturn short circuit: An electrical fault in which adjacent turns of the same stator winding phase become electrically connected, causing localised current loops and magnetic asymmetries.

Analytical redundancy: A model-based diagnostic principle that generates expected sensor signals from a mathematical representation of the system and compares them to actual measurements to form residuals.

Instantaneous reactive power (IRP): A time-domain quantity computed from the product of phase voltage and the Hilbert-transformed current, used to characterise energy exchange between the stator and magnetic field.

Structural analysis: A method for partitioning a system of dynamic equations into solvable and over-determined subsets to derive diagnostic relations and residuals sensitive to specific fault terms.

References

1. A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines. Energies (2018).
2. Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs. IEEE Access (2020).
3. Real-Time Detection of Incipient Inter-Turn Short Circuit and Sensor Faults in Permanent Magnet Synchronous Motor Drives Based on Generalized Likelihood Ratio Test and Structural Analysis. Sensors (2022).
4. Detection of Stator Winding Faults in PMSMs Based on Second Harmonics of Phase Instantaneous Reactive Powers. Energies (2022).
5. Interturn Short-Circuit Fault Detection of a Five-Phase Permanent Magnet Synchronous Motor. Energies (2021).
6. A Novel Stator Turn Fault Detection Technique by Using Equivalent High Frequency Impedance. IEEE Access (2020).