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As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield various joint failures and result in significant downtime costs. Targeting the most common robotic joint brushless DC (BLDC) motor-drive systems, this paper proposes a robust online diagnostic method for semiconductor faults for BLDC motor drives. The proposed fault diagnostic technique is based on the stator current signature analysis. Specifically, this paper investigates the performance of the BLDC joint motors under open-circuit faults of the inverter switches using finite element co-simulation tools. Furthermore, the proposed methodology is not only capable of detecting any open-circuit faults but also identifying faulty switches based on a knowledge table by considering various fault conditions. The robustness of the proposed technique was verified through extensive simulations under different speed and load conditions. Moreover, simulations have been carried out on a Kinova Gen-3 robot arm to verify the theoretical findings, highlighting the impacts of locked joints on the robot’s end-effector locations. Finally, experimental results are presented to corroborate the performance of the proposed fault diagnostic strategy. 

High-performance switching devices like SiC MOSFETs introduce high-frequency ringing and overvoltage transients at motor terminals, leading to uneven voltage distribution across windings. In SiC-driven motors, the first coil and initial turns experience significant overvoltage stress, increasing the risk of insulation degradation and inter-turn faults. This study proposes an analog circuit to mitigate overvoltage stress. The circuit detects high dv/dt in the first coil and adaptively inserts a ceramic capacitor via a GaN switch, forming a low-impedance path for high-frequency currents. This diverts part of the transient energy to the second coil, reducing stress on the first coil and promoting uniform voltage distribution. The GaN switch remains closed to sustain the high-frequency current path through the capacitor, adapting to different operating conditions and cable lengths. The circuit was prototyped and experimentally validated on a 2hp induction motor driven by a SiC inverter, demonstrating its effectiveness in mitigating overvoltage stress. This compact solution enhances the reliability of SiC-driven motor systems by addressing uneven high-frequency voltage distribution. 

High dv/dt from the emerging SiC variable-frequency drives can easily induce overvoltage across the motor stator winding terminals, especially for long-cable-connected and high-voltage motor-drive systems. Due to the fast switching speed and surge impedance mismatch between cables and motors, this overvoltage can be two times or even higher than the DC-bus voltage of the inverter, resulting in motor insulation degradation or irreversible breakdown. The most common solution to mitigate such overvoltage is to install a dv/dt or a sinewave filter at the output of the drive, which decreases the efficiency and power density of the system. Among different stator coils, the first one (close to the drive side) is the most susceptible to insulation breakdown since it experiences higher overvoltage than the others due to the nonlinear distribution of the reflected surge voltages. In this paper, an innovative high-efficiency ultracompact mitigation solution is introduced, which is a tiny auxiliary circuit embedded inside the motor stator (or at the motor terminal box), specifically across the first few coils of each phase (i.e., smart coils). The proposed smart coil circuit effectively mitigates the surge overvoltage, which can be scalable to any type of motor-drive systems, regardless of cable length and semiconductor rise time. The proposed solution can dramatically improve the reliability, efficiency, and power density of motor-drive systems.