| Title |
Performance Evaluation of the Continuos Wavelt Transformation Data in Motor Fault Diagnosis through XAI Algorithm |
| Authors |
한지훈(Ji-Hoon Han) ; 박상욱(Sang-Uk Park) ; 홍선기(Sun-Ki Hong) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.1.225 |
| Keywords |
Motor fault diagnosis; LRP; XAI; Deep Learning; Wavelet Transformation; Preprocessing |
| Abstract |
The data mainly used for motor fault diagnosis is FFT. However, preprocessing such as continuous discrete wavelet transform is used. When using deep learning algorithms, the performance of the data is evaluated by the model output. However, in order to improve the learning possibilities and preprocessing performance of data, performance evaluation from a model perspective is required. For this purpose, data performance evaluation using the LRP algorithm, one of the XAI techniques, is studied. Initial fault state diagnosis using current data, which is difficult to solve with FFT data, is performed with STFT and CWT data, and performance is evaluated through LRP. Experimental Results STFT and CWT are preprocessing techniques that enable the use of current signals for early fault diagnosis. Among the two preprocessing methods, the use of CWT is more preferable because the flexibility of the preprocessing is increased. |