# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
###### Abstract  
The permanent magnet synchronous motor (PMSM) has been used in electric propulsion and other fields. However, it is prone to the stator winding inter-turn short-circuit, and if no effective measures are taken, the ship's power system will be paralyzed. To realize intelligent diagnosis of inter-turn short circuits, this paper proposes an intelligent fault diagnosis method based on improved variational mode decomposition (VMD), multi-scale principal component analysis (PCA) feature extraction, and improved Bi-LSTM. Firstly, the stator current simulation dataset is obtained by using the mathematic model of the inter-turn short-circuit of PMSM, and the parameters of VMD are optimized by the grey wolf algorithm. Then, the data is coarse-grained to obtain multi-scale features, and the main features are selected as the sample data for fault classification by PCA. Subsequently, the Bi-LSTM neural network is used for training and analyzing the data of the sample set and the test set. Finally, the learning rate and the number of hidden-layer nodes of the Bi-LSTM are optimized by the whale algorithm to increase the diagnosis accuracy. Experimental results show that the accuracy of the proposed method for inter-turn short-circuited fault diagnosis is as high as 100%, which confirms the effectiveness of the method. fault diagnosis; permanent magnet synchronous motor; interturn short-circuited; variational mode decomposition; multi-scale; bidirectional long short-term memory neural network +
Footnote †: journal: Medical Imaging  
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Footnote 1: email: _Applied_  
Footnote †: journal: Medical Imaging  
to analyze the stator current signal to obtain the dynamic eccentric state characteristic frequency of the stator current signal and extract the amplitude. Finally, the support vector machine with parameter optimization by a genetic algorithm is used to identify the extracted features. Most of the existing research on mechanical faults of PMSM can only detect a single fault type, and there are few studies on the application of variable speed conditions. [PERSON] et al. [6] proposed a mechanical fault detection method for PMSM based on improved detrended fluctuation analysis (DFA) and linear discriminant analysis (LDA). The Vold-Kalman is used to track and extract the fault feature components at the fault feature frequency, and the fault feature components are reconstructed. The improved DFA is used to extract the feature of the reconstructed signal. Finally, LDA is conducted to classify the extracted fault features. Aiming at the problem that open-circuit faults in inverters and stators are difficult to be detected, resulting in overstressing healthy switches, large torque ripples, and mechanical vibrations, [PERSON] et al. [7] proposed a multi-break fault diagnosis method for three-phase PMSM based on symmetrical components and DC components. To evaluate the asymmetry and discriminate fault types, the magnitude ratio of the positive sequence to the negative sequence of phase currents is adopted as a fault detection index. At last, the DC components of the phase currents in different reference frames are utilized to locate faults. In recent years, with the rapid development of deep learning, its powerful feature extraction ability and end-to-end characteristics make it possible to directly extract fault features from the original signal for fault diagnosis tasks, avoiding the influence of artificial experience on feature extraction and effectively overcoming the disadvantages of traditional diagnosis methods [8]. Aiming at the problems of difficulty in capturing inverter faults and poor fault diagnosis accuracy in PMSM, [PERSON] et al. [9] proposed a fusion stacked denoising autoencoder and feedforward neural network (SDA-FFNN) model. Through normalization, six frequency-domain features are integrated into a high-dimensional feature data set, and then the proposed method based on SDA-FFNN is used to complete fault diagnosis.