Permanent magnet synchronous machines (PMSMs) are widely used in various industries such as transportation, manufacturing and renewable energy. The simple structure of direct torque control (DTC) coupled with its encoderless operation and fast dynamics are of great interest for PMSMs. Nevertheless, the occurrence of faults, such as turn-to-turn short circuit, high resistance contact, static eccentricity and partial demagnetization, remains a concern. Faults can prevent smooth drive operation of DTC and potentially lead to catastrophic losses if not detected and mitigated in their early phases. Hence, fault diagnosis of DTC driven PMSMs is paramount to ensuring reliable drive operation.An essential aspect of developing effective fault diagnosis is to understand the impact of faults on drive operation and its corresponding reaction. A comprehensive examination of the nonlinear behavior of flux and torque hysteresis comparators in DTC driven PMSMs provides insight. It is shown that DTC can tolerate low-severity faults within the controller bandwidth while continuing to operate normally. However, when flux and torque errors exceed the bandwidth, DTC counteracts by introducing negative sequence voltages and torque angle variations which impacts fault diagnosis and control under faulty conditions.Many existing fault diagnosis methods are based on field oriented control (FOC); however, it is not well understood how these methods translate to DTC driven PMSMs. Machine Voltage Signature Analysis (MVSA) is the most commonly used approach for fault diagnosis in electric machines. However, the use of DTC introduces challenges for adoption MVSA due to its nature of compensation, structure and regulation principle. A novel fault diagnosis approach for DTC driven PMSMs is developed. This approach maintains the simple structure of DTC, removes the need for complex signal processing tools, and relies solely on the available signals in the drive. The occurrence of faults results in unique deviations in the direction and magnitude of the commanded voltages in the stator flux linkage (MT) frame enabling fault detection, classification, and severity assessment.Ultimately, the fault diagnosis algorithm used for inverter driven PMSMs should be effective and applicable irrespective of the control type. A comprehensive fault diagnosis approach is developed based on active and reactive power signature analysis. This data driven algorithm uses spectral components of the power signals as fault indicators. It is shown that this developed algorithm is capable of fault diagnosis in both FOC and DTC driven PMSMs.The reliability of inverter driven PMSMs depends on the ability to monitor its state of health during operation. It is necessary to detect that a fault occurred, identify fault type as well as estimate its severity. Classification algorithms are used to separate fault types and estimate fault severity. Here, the performance of three classification algorithms is evaluated for inverter driven PMSMs. The classification algorithms are linear discriminate analysis (LDA), k-nearest neighbor (k-NN), and support vector machines (SVM). The SVM classifier is shown to be a highly effective method for detecting and classifying faults in PMSMs controlled by either drive, even with limited training data and high noise levels.
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