Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 22; p. 8958
• Main Authors: Toma, Rafia Nishat, Gao, Yangde, Piltan, Farzin, Im, Kichang, Shon, Dongkoo, Yoon, Tae Hyun, Yoo, Dae-Seung, Kim, Jong-Myon
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.11.2022; MDPI
• Subjects: Acoustics; Algorithms; Analysis; artificial neural network; bearing fault diagnosis; Classification; Coefficients; condition monitoring; Data integration; Data science; Discriminant analysis; extreme gradient boosting; Fault diagnosis; Fourier transforms; Induction electric motors; induction motor; Induction motors; Machine Learning; Methods; motor current signal; Neural networks; Neural Networks, Computer; Preventive maintenance; Scattering; Sensors; Signal classification; Signal processing; Support vector machines; Wavelet Analysis; Wavelet transforms; South Korea; artificial neural network; induction motor; motor current signal; extreme gradient boosting; condition monitoring; wavelet scattering transform; random forest; bearing fault diagnosis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22228958

In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extraction techniques are sensitive to the noisy component of the signal and need more time for training. To deal with these issues, a comparatively new feature extraction technique, referred to as a wavelet scattering transform (WST) is utilized, and incorporated with ML classifiers to design a framework for bearing fault classification in this paper. The WST is a knowledge-based technique, and the structure is similar to the convolution neural network. This technique provides low-variance features of real-valued signals, which are usually necessary for classification tasks. These signals are resistant to signal deformation and preserve information at high frequencies. The current signal data from a publicly available dataset for three different bearing conditions are considered. By combining the scattering path coefficients, the decomposition coefficients from the 0th and 1st layers are considered as features. The experimental results demonstrate that WST-based features, when used with ensemble ML algorithms, could achieve more than 99% classification accuracy. The performance of ANN models with these features is similar. This work exhibits that utilizing WST coefficients for the motor current signal as features can improve the bearing fault classification accuracy when compared to other feature extraction approaches such as empirical wavelet transform (EWT), information fusion (IF), and wavelet packet decomposition (WPD). Thus, our proposed approach can be considered as an effective classification method for the fault diagnosis of rotating machinery.