Inter-turn fault (ITF) is a common fault for permanent magnet synchronous machine (PMSM). If the ITF is not detected in time, its propagation may result in the secondary damage, even the damage to the whole electrical machine. Recently, model predictive control (MPC) has been widely studied for the PMSM drive system because of its simple structure and excellent dynamic performance. However, the previous ITF diagnosis methods are mainly focused on vector control and direct toque control systems, and few researches are specifically focused on ITF diagnosis for the model-predictive-controlled-PMSM. Hence, to fill this gap and improve the reliability of the model-predictive-controlled-PMSM, this paper first proposes a ITF diagnosis method for the model-predictive-controlled-PMSM, taking into account the characteristics of the MPC system. In this method, wavelet transform is applied to extract the fault feature from the cost function existing in the MPC system, and the ITF is diagnosed by monitoring the normalized energy-related feature vector calculated from the wavelet transform coefficients. Additionally, to further show the performance of the proposed method, the popular diagnosis methods based on the stator current and zero sequence voltage component are studied for the comparative analysis. Both the simulation and experimental results verify the effectiveness of the proposed fault diagnosis method.

Open-circuit fault is one of the most common faults in permanent-magnet synchronous machine (PMSM) drives. The open-circuit fault can cause the obvious change of stator currents of the PMSM. Hence, the previous artificial-intelligence-based-fault diagnosis method mainly relies on the samples extracted from stator currents. However, the large sets of the samples are required due to the variation of the PMSM operating point, increasing the complexity of fault diagnosis. What is more, stator currents are easily affected by the noise, decreasing the accuracy of fault diagnosis. To solve the issues, this article proposes a robust open-circuit fault diagnosis method using the wavelet convolutional neural network with small samples of the normalized current vector trajectory graph. The proposed method uses current normalization to establish small sample sets and combines the convolutional neural network with discrete wavelet transform to enhance the robustness to noise. The proposed fault diagnosis method is validated by simulation and experiment. Both the results show that the proposed method can effectively diagnose 22 kinds of open-circuit fault types (including healthy mode), being with great antinoise ability and robustness to different working conditions.

Open-phase fault (OPF) in the permanent magnet synchronous motor (PMSM) drive system is one of the most prevalent failures due to the resulting large electromagnetic torque ripples and serious mechanical vibration. To improve the reliability of the PMSM drive system, several OPF diagnosis methods were studied and were mainly focused on vector control system. Recently, model predictive control (MPC) has been emerging as a popular control method for the PMSM drive system owing to its simple structure and excellent dynamic performance. However, so far, there are no special OPF diagnosis methods for the model-predictive-controlled-PMSM drive system. Hence, this paper first proposes a cost function-based OPF diagnosis method for the model-predictive-controlled-PMSM drive system, considering the characteristics of MPC system. The DC component and second harmonic component in the cost function are used for the OPF diagnosis, and the initial phase angle differences are defined to locate the faulty phase. The experimental results show that, by the proposed fault diagnosis method, not only the OPF can be rapidly detected, but also the faulty phase can be effectively located. Moreover, the proposed method can be implemented simply and is robust against the operating point and parameter variations of the PMSM drive system.

Fault diagnosis is essential for the safe operation of a permanent magnet synchronous machine (PMSM) system. At present, the fault diagnosis method based on deep learning has been gradually studied due to its ability to automatically extract fault features and an end-to-end diagnostic model. However, this method often uses a single-source signal, resulting in poor fault diagnosis performance. On the other hand, deep learning model typically requires large amounts of storage and computational resources, especially when processing multisource signals. Hence, this article proposes an improved fault diagnosis method for PMSM systems based on lightweight multisource information data layer fusion. In this method, the original multisource information is fused at the data layer. Then the fused information is input into the constructed lightweight deep learning model to implement fault diagnosis of PMSM system, where a one-dimensional convolutional neural network with the combination of depth-separable convolution and global average pooling is first presented to reduce the computational time and complexity of the fault diagnosis method. The simulation and experimental results show that the proposed fault diagnosis method can achieve accurate fault identification of the PMSM system and can reduce the requirement for storage and computational resources, indicating the effectiveness of the proposed fault diagnosis method.

Partial demagnetization fault (PDF) is a common problem for permanent magnet synchronous motor (PMSM). The PMSM usually operates under complex working conditions (dynamic speed and various load), leading to the difficulty in robust PDF diagnosis. Hence, how to reliably and accurately diagnose PDF under complex working conditions has become a key issue in ensuring its safe operation. To address this issue, a robust PDF diagnosis method for PMSM is proposed based on radial air-gap flux density in this article. First, the d -axis magnetic network model of PMSM is established to extract the fault feature from the radial air-gap flux density. Then, by subtracting the offline-calculated radial air-gap flux density of stator current excitation from the online measured value, the open-circuit radial air-gap flux density can be calculated. Next, the equiangular interval resampling method is used to obtain the open-circuit radial air-gap flux density with respect to rotor position angle. Finally, through analysis of open-circuit radial air-gap flux density in angular coordinates, not only PDF can be detected and the fault degree can be estimated, but also the demagnetized PM can be located under complex working conditions. Both the simulation and experimental results validate the effectiveness of the proposed PDF diagnosis method.

This paper proposes an equivalent conversion-based improved loss minimization control method for interior permanent magnet synchronous motor (IPMSM). In the proposed control method, both the copper loss and iron loss are studied and the efficiency optimization problem due to the unequal inductance parameters of IPMSM are considered and are solved, thus achieving the optimal efficiency. First, according to the mathematical model of the IPMSM considering iron loss resistance, the equivalent conversion method is presented to derive the relationship between electromagnetic torque, d-axis and q-axis currents. Then, by the polynomial fitting method, the d-axis and q-axis currents which minimize the electrical loss (copper loss and iron loss) are calculated. Additionally, to further show the performance of the proposed control method, the MTPA control method and the approximate processing control method are studied for the comparative analysis. Both the simulation and experimental results show that the proposed control method can comprehensively reduce the electrical loss of the IPMSM, showing the effectiveness of the proposed control method.