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Demagnetization and eccentricity are unique and common rotor faults of permanent magnet synchronous motors, respectively. These important factors affect reliability and security of motor drive systems. Online monitoring and real-time diagnosis are typically required in highly reliable application scenarios. The influence of demagnetization and eccentricity faults on spatiotemporal distribution of stator tooth flux based on a stator magnetic flux detection device with multiple search coils is analyzed in this article by establishing a mathematical model of stator tooth flux with corresponding faults. Moreover, a method for extracting rotor fault features and formulating the corresponding fault diagnosis is proposed. Simulations and experiments showed that the proposed diagnosis method can effectively identify the corresponding fault type and diagnose the number of demagnetization poles, degree of demagnetization, and magnitude and orientation of eccentricity ratio. The proposed method can provide an important basis for the operation and maintenance of the motor, help reduce losses due to faults, and improve reliability of the motor and the entire system. [ABSTRACT FROM AUTHOR]

The bearing vibration of wind turbines is nonlinear and non-stationary. To effectively extract bearing vibration signal features for fault diagnosis, a method of feature vector extraction based on variational mode decomposition (VMD) and energy entropy is proposed. In addition, the support vector machine (SVM) classifier is used to identify the types of vibration faults. VMD transforms the constrained variational objective function into the unconstrained one to optimize solution. Compared with the VMD and empirical mode decomposition (EMD), the modal decomposition layer of VMD is less than EMD, no found false modality, and truly reflecting signal components. After the Hilbert transformation, double logarithmic coordinates show that the VMD-based spectral characteristics are significant. VMD is performed on the different types of vibration signals of wind turbines. Therefore, VMD is not that affected by noise and has few decomposition levels. The energy entropy of the normalized four modal components is considered the eigenvalue and classified by SVM, and compared with EMD-based and wavelet db4-based energy entropy eigenvalue extraction methods. Experimental results indicate that the accuracy of the method is higher than those based on EMD and wavelet db4, under the limited sample condition. Thus, a referential diagnostic method is provided for practical applications. • A method of feature vector extraction based on variational mode decomposition (VMD) and energy entropy is proposed. • The time-frequency energy spectrum obtained by VMD is compared with that by empirical mode decomposition (EMD). • Different types of bearing vibration signals of wind turbine are analyzed. • The fault diagnostic identification of the proposed method is compared with EMD-based and wavelet db4-based methods. [ABSTRACT FROM AUTHOR]

This study proposes a novel inter‐turn fault diagnosis method for a permanent magnet synchronous machine. Search coils are set on every stator tooth to measure the tooth fluxes. The high‐order harmonics produced by the inverter are adopted to diagnose the inter‐turn fault existence and locate the fault tooth. The fault severity coefficient is proposed on the basis of theoretical analysis and can identify the fault severity. A comparative[AQ ID=Q1] analysis of a traditional method based on co‐simulation shows that the proposed method is sensitive and that the fault severity coefficient is accurate, intuitive and independent of the operating condition of the motor. An experimental platform is set up, and it validates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]