# A Diagnostic Method for the Saturable Reactor Core Looseness Degree of Thyristor Converter Valves
###### Abstract  
During the long-term operation of thyristor converter valves, the saturable reactor vibration (mainly caused by magnetostriction) will lead to core looseness faults. In order to accurately evaluate the fault degradation degree, this paper proposes a vibration signal recognition model for iron core looseness based on synchrosqueezed wavelet transforms and a convolutional neural network. Firstly, vibration experiments are conducted on saturable reactors to obtain signals under different core looseness degrees. Then, the spectrogram features of vibration signals are extracted using synchrosqueezed wavelet transform. Finally, based on the high-dimensional learning ability of convolutional neural networks, the fault characteristics of the spectrogram are mined to accurately identify the core looseness degree. The research results indicate that the model in the paper has higher recognition accuracy than some other methods, which provides convenience for the monitoring and maintenance of saturable reactors. s _applied_  
_sciences_  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] A Diagnostic Method for the Saturable Reactor Core Looseness Degree of Thyristor Converter Valves. **Explore**  
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there is no current measurement device installed on the bridge arm of the converter valve. It fails to obtain the operating conditions of the converter valve. Therefore, it is not feasible to identify the vibration signal of the saturable reactor core looseness degree based on the independent operating conditions. The extraction and recognition of vibration features based on variable operating conditions is an important goal of this study. Traditional time and frequency domain analyses and machine learning algorithms cannot realize accurate identification of core looseness. Because the data features under varying operating conditions are mixed, misjudgments can occur. In recent years, convolutional neural networks (CNNs) have been widely used in the field of power equipment, including for feature extraction, pattern recognition, parameter prediction, etc. [7; 8; 9]. A convolutional neural network can effectively avoid the problem of the exponentially increased number of neurons by reducing the dimensions of input features through convolution, pooling and other processes. By constructing a deep-level network structure, the feature information contained in the input data can be effectively learned and beneficial features can be retained. Therefore, this paper builds a multi-layered convolutional neural network to deeply study the vibration characteristics of the saturable reactor core and effectively overcomes the problem of misjudgments. In addition, research on vibration signals mainly focuses on feature extraction in the time and frequency domains, such as signal decomposition, spectrum analysis, etc. [10; 11]. In order to extract richer feature information, [PERSON] et al. constructed a two-dimensional image with rich time-frequency information [12]. The image was based on the vibration signal of bearings, which applied continuous wavelet transform (CWT). [PERSON] et al. transformed the vibration dataset of bearings and gears into two-dimensional grayscale images to improve the diagnosis accuracy of proposed models [13]. According to the above research, this paper also converts the one-dimensional signals into a 2D spectrogram image and takes the image as the direct input of a CNN. Synchrosqueezed wavelet transform (SST) is an improved method of wavelet transform with a better compression rate [14]. [PERSON] et al. applied SST to extract gear fault features and improved the resolution of time-frequency representation through multi-step dual kernel denoising [15]. [PERSON] et al. improved the SST algorithm to extract the rapidly changing intermediate frequency signal of aviation generator vibration [16]. [PERSON] et al. compared the feature expression effects of SST and HHT, verifying the effectiveness of SST in extracting fault features from non-stationary signals in rotating machinery [17].