In this paper incremental inductance approach is used for static eccentricity detection under offline condition. This method uses the DC values of q-axis voltage that makes it independent of the bandwidth of the controller. In this work a new feature, height of hump of the incremental inductance curve, along with the known feature, saturation current in d-axis, was used for fault detection. It is shown that this new feature is independent of the machine operating temperature that makes the method robust with respect to machine operating temperature. It is also shown that the proposed method is robust to the rotor position misalignment. A new controller for off-line testing was designed that controls both the speed and saturation of the machine. Using this controller the hardware requirements for constant rotation of the machine are reduced, while keeping the method independent of the bandwidth of the controller. Finally, a Matlab based computer program was developed to calculate the features: saturation current in d-axis and height of hump, of the incremental inductance curve. Six different machines, combinations of two stators and three rotors, were used to validate the proposed method through experiments.

Condition monitoring and preventative maintenance are essential for reliable and efficient operation of permanent magnet synchronous machines driven by inverters. There are two types of industrial inverter drives available: field oriented control and direct torque control. Their compensation nature and control structure are distinct and, therefore, the condition monitoring approach designed for the former control may not be applicable to the latter one. In this paper, we investigate the Motor Voltage Signature Analysis approach for both inverter drives under healthy and faulty conditions. Four typical fault conditions are addressed: turn-to-turn short circuit, high resistance contact, static eccentricity, and local demagnetization. High fidelity cosimulation is developed by coupling the finite element machine model with both control drives. The spectral elements of the commanded stator voltage are utilized as indicators for supervised classification to identify, categorize, and estimate the severity of faults. Linear discriminate analysis, k-nearest neighbor, and support vector machines are the classification techniques used. Results indicate that the condition monitoring based on the Motor Voltage Signature Analysis performs adequately in field oriented control. Nevertheless, the utilized monitoring scheme does not exhibit satisfactory performance in direct torque control owing to the nonlinear characteristics and tolerance nature of this drive.

Direct-torque-control-driven permanent magnet synchronous machines eliminate the need for a position sensor while providing improved torque dynamics. However, the structure, regulation principle and nature of compensation of hysteresis-based controllers used in direct torque control impacts performance under faulty operating conditions. This paper analyzes the reaction of direct torque control to the presence of various faults that occur in permanent magnet synchronous machines. The analysis presented reveals that the direct torque control injects a negative sequence voltage and manipulates the torque angle to meet the control objectives when a fault occurs. The co-simulation of finite element analysis and a multi-physic circuit simulator is used to validate the response of the hysteresis-based controller to the machine health. The results indicate that the hysteresis comparators have the ability to mask the impact of the faults in the direct-torque-control-driven permanent magnet synchronous machines.

Presented in this paper is a mitigation technique for an open-circuit fault (OCF) in a closed-loop Volt-per-Hertz controlled three-phase induction motor. Conventional proportional–integral (PI) controllers have been found inadequate for maintaining stable motor performance during the fault and exhibit significant transient issues when transitioning from fault to normal operation. To address these limitations, a proportional–resonant (PR) control method and a proportional–integral–resonant (PIR) control method are proposed. The PIR controller enhances the traditional PI controller by integrating a resonant component, enabling effective performance during the fault and improving transient responses during pre-fault conditions. Experimental validation using a dSPACE DS1104 platform demonstrates that the PR and PIR control methods significantly improve motor performance compared to the PI method. The proposed approaches eliminate the need for fault detection, offering a simpler and cost-effective alternative for maintaining motor reliability and efficiency under fault conditions. These results underscore the potential of the proposed method as a robust solution for the fault scenarios in industrial applications.

This paper presents an enhanced fault-tolerant control framework for induction motor drive systems, emphasizing resilience, adaptability, and system-level reliability in interconnected industrial environments. A modified field-oriented control structure is developed to ensure decoupled flux and speed regulation under open-circuit fault conditions. The proposed control integrates an adaptive proportional–integral–resonant (PIR) controller within a reconfigured stationary reference frame to maintain stable operation even during partial phase loss. By continuously adjusting its resonant frequency in response to real-time feedback, the controller achieves superior transient and steady-state performance. Experimental validation on a dSPACE DS1104 real time platform compares the performance of PI, PR, and PIR controllers. Results reveal that the conventional PI controller fails to sustain post-fault operation, while the adaptive PR controller enhances system continuity and dynamicrecovery. The proposed adaptive PIR controller further mitigates torque pulsations, reducing torque ripple from over 60% (PI-based) to approximately 10%, demonstrating significant improvement in fault-tolerant performance and robustness. This approach contributes to the development of reliable, self-healing industrial drive subsystems for next-generation smart manufacturing systems.