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

Abstract

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.

Wavelet scattering‐ and object detection‐based computer vision for identifying dengue from peripheral blood microscopy

“…In our model, we used the Morlet wavelet as the mother wavelet. The data were first convolved with the scaling function to yield the zero th ‐order scattering coefficients 47,48 where i represents input data; φ, scaling function, and S[0], zero th ‐order scattering coefficients.…”

“…The input data were then subjected to wavelet transform using each wavelet filter in the first filter bank, and the modulus of each filtered output was calculated. These moduli were averaged with the scaling filter to yield the first‐order scattering coefficients 47,48 where {ψj,k} represents the wavelet, and S [1], first‐order scattering coefficient.…”

“…The wavelet filters encompassed different scales and rotations with respect to the image, which enabled a comprehensive analysis of the input data. By iterating the above steps, the second‐order scattering coefficients were computed 47,48 where S [2] represents second‐order scattering coefficients, they were then used to decompose the PBS image (Figure 2).…”

“…These moduli were averaged with the scaling filter to yield the first-order scattering coefficients. 47,48 S 1…”

“…The data were first convolved with the scaling function to yield the zero th -order scattering coefficients. 47,48 S 0…”