# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
###### Abstract
To address the issue that motor-bearing fault diagnosis requires a complex feature extraction process and suffers from a low fault diagnosis recognition rate under various working conditions, [PERSON] et al. [8] proposed a fault diagnosis method based on attention and a multi-scale convolutional neural network. In this method, convolution kernels of different sizes are used to extract multi-scale features of fault signals so that the model can obtain richer and complementary fault feature representations. However, multi-scale feature extraction will lead to unstable time-frequency domain feature results due to mutations at breakpoints. To solve the problem that the frequency-domain characteristics of the stator current change with the switching of motor operating conditions, which leads to the difficulty of diagnosis under multiple operating conditions, [PERSON] et al. [10] proposed a fault diagnosis method with a convolutional neural network (CNN)-based on a phase tracker. The CNN-based phase tracker is used to convert the original signal from the time domain to the angle domain. After the angle-domain resampling, the fundamental frequency of the stator current signal under any working condition will be adjusted to the same frequency so that the fixed frequency-domain fault characteristics can be used for motor fault diagnosis. It is mentioned in this paper that CNN is usually used as a feature extraction model for time series in motor fault diagnosis [11; 12]. The stator winding inter-turn short circuit fault is the most common fault of PMSM. If the fault is not detected in time and no measures are taken, the operation with a fault will increase the motor current, which will produce more heat, increase the temperature of the motor, and further destroy the insulation material and permanent magnet material, resulting in a more serious short circuit and loss of excitation [4]. A lot of research has been conducted on the stator winding inter-turn short circuit fault. [PERSON] et al. [13] proposed an initial inter-turn short circuit fault detection method for PMSM based on VMD and logarithmic spectrum analysis. The zero-sequence voltage is taken as the fault feature, and the noise and the related harmonic components in the zero-sequence voltage component are removed by VMD to highlight the fault component. However, in practical applications, it is difficult to select the decomposition layer and penalty factor of VMD, and it is challenging to obtain the optimal solution of the two parameters. Aiming at the problems of small fault data, unbalanced data distribution, and low data quality of inter-turn short circuit fault data in industrial production environments, [PERSON] et al. [14] proposed an inter-turn short circuit fault diagnosis algorithm based on the stacked sparse autoencoder network. The negative sequence current and electromagnetic torque are used to construct the inter-turn short circuit fault diagnosis data set, and the stacked sparse autoencoder is employed to extract the hidden abstract features in the fault data. Finally, the SoftMax classifier is used to classify the feature signals. [PERSON] et al. [4] presented a permanent magnet synchronous motor fault diagnosis method based on mixup-LSTM. The fault features in the stator current signal are extracted by wavelet packet decomposition as the sample set, and the sample expansion is realized by mixup. Finally, the LSTM neural network is exploited to realize fault diagnosis (classification). As a variant of the RNN neural network, LSTM can solve the problem of gradient disappearance of the traditional RNN. Although wavelet packet decomposition has good analysis ability, it cannot suppress errors, and more accurate diagnosis results may not be obtained in the subsequent diagnosis process. Moreover, the number of layers and nodes of the hidden layer depends too much on artificial experience, and the optimal solution cannot be obtained.