This paper presents an improved virtual space vector modulation (IVSVM) scheme for a three-level active neutral-point-clamped (3L-ANPC) inverter to balance neutral-point potential (NP), reduce switching loss, and suppress common mode voltage (CMV). In the IVSVM scheme, an improved virtual medium vector (VMV) is synthesized by the original medium vector and the adjacent two pairs of the small vectors since there are always two pairs of the small vectors in any regions of the new space vector diagram. The improved VMV provides the flexibility to control the NP balancing under any modulation indexes and power factors in one switching cycle while achieving SL reduction and CMV suppression. This is achieved by the proper selection of two pairs of small vectors in terms of NP charge and the pulse sequence. The experimental platform based on a 3L-ANPC inverter is established and results obtained to verify the effectiveness of the IVSVM control strategy.

A multisensor data fusion method is investigated for bearing fault diagnosis of a switched reluctance motor (SRM) of an electric vehicle (EV) powertrain under varying speed conditions. The accumulative rotation angle of the SRM rotor is estimated by fusing the synchronous sampled current and vibration signals. The time-domain vibration signal is then resampled on an angular domain, and the bearing fault type is identified on the envelope spectrum of the resampled signal. In this article, an experimental setup is designed to validate the performance of the proposed method compared with the traditional ones. The practical EV working conditions including driving, coasting, and braking are considered in the experiments. Results indicated that the proposed method successfully diagnoses the SRM bearing faults under random and complex conditions. The method is promising for online SRM fault diagnosis under varying speed conditions as it requires no extra tachometer, specifically when the sensorless control strategy is adopted.

Continuous condition monitoring and fault diagnosis of motor bearings are vital to guarantee motor safety operation and reduce breakdown losses. With numerous Internet of things (IoT) sensors being installed on motors for condition monitoring, data transmission and storage problems have become new challenges. This study designed a signal enhancement and compression (SEC) method and implemented on an IoT platform for motor bearing fault diagnosis. First, vibration signal is acquired from an accelerometer installed on the motor. The bearing signal is demodulated using an online demodulation algorithm. Second, an envelope signal is downsampled and enhanced using a stochastic resonance-based nonlinear filter. The enhanced signal is compressed using an Opus encoder and transmitted to a receiver. Lastly, the received signal is decompressed using the Opus decoder, and the bearing fault type can be recognized. The effectiveness and efficiency of the proposed SEC method are verified on an IoT platform compared with a conventional method. The proposed method improves 3.83 dB of the average signal-to-noise ratio (SNR), and reduces 94.7% of the total time and 94.6% of the dissipative power. The advantages of the proposed SEC method include high output SNR, low power consumption, and compatibility with edge computing. These advantages show potential applications in remote motor fault diagnosis using battery power supply.

This article proposes an integration method for interturn fault diagnosis and torque ripple minimization control of direct-torque-controlled surface-mounted permanent magnet synchronous machine (SPMSM) drive system without requiring the specific fault severity (short-circuited turn ratio, current). In this method, the interturn fault is detected and located based on the amplitude and initial phase angle of the zero-sequence voltage component (ZSVC). Both the torque injection and the improved flux observer are applied to reduce the torque ripple caused by the interturn fault. Furthermore, the torque injection and the improved flux observer are directly calculated by using the already obtained amplitude and initial phase angle of the ZSVC during the fault diagnosis process, without requiring the specific values of short-circuited turn ratio and fault current. The experimental results validate the effectiveness of the proposed integration method for the direct-torque-controlled SPMSM drive system.

An efficient data reduction algorithm is designed and implemented on an industrial Internet of Things (IIoT) node for permanent magnet synchronous motor (PMSM) bearing fault diagnosis in variable speed conditions. Leakage flux and vibration signals are, respectively, acquired by a magnetic sensor and an accelerometer on the IIoT node in a noninvasive manner. These two signals are processed and mixed on the IIoT and transmitted to a server. The received signal is separated, the cumulative rotation angle is calculated, and the vibration signal is resampled for bearing fault identification. The proposed method can reduce about 95% of the transmission data while maintaining sufficient precision in bearing fault diagnosis in comparison to a traditional method. The proposed method based on edge computing reduces the power consumption, and hence it is suitable to use on a battery-supplied IIoT node for remote PMSM condition monitoring and fault diagnosis.

This paper proposes a DC-flux-injection method for fault diagnosis of high-resistance connection (HRC) in direct-torque-controlled permanent magnet synchronous machine (PMSM) drive system. In this method, the flux linkage bias is superimposed in the flux control loop of the direct torque control (DTC) system, and then the DC current offset is excited in the stator windings. By superimposing two different pairs of flux linkage biases, a binary linear equation group about the resistance deviations can be established based on the stator currents and zero sequence voltage component. By solving the equation group, the resistance deviations can be calculated for the HRC fault detection, faulty phase location and fault severity estimation. Both the simulation and experimental results validate the effectiveness of the proposed method. In addition, the proposed method is immune to the symmetrical variations of stator resistance and inter-turn fault of the PMSM.