Research Article Fault Size Estimation of Bearings Using Multiple Decomposition Techniques with Artificial Neural Network Suchi Mishra , 1 Rahul Dubey, 2 Preety D. Swami, 3 and Alok Jain 1 1 Department of Electronics and Instrumentation Engineering, SATI, Vidisha, India 2 Department of Electronics Engineering, MITS, Gwalior, India 3 Department of Electronics and Communication Engineering, UIT RGPV, Bhopal, India Correspondence should be addressed to Suchi Mishra; mishrasuchi08@gmail.com Received 14 April 2022; Revised 16 May 2022; Accepted 30 May 2022; Published 17 June 2022 Academic Editor: Punit Gupta Copyright © 2022 Suchi Mishra et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A running machine generates multi-frequency vibration signals which can be captured by accelerometers. Empirical mode decomposition, wavelet decomposition, and wavelet packet decomposition are the commonly used methods to decompose the multi-frequency signal. Quick fault classification, accurate signal decomposition, and fault size detection are still a problem in machines with rotary components. In the proposed work, fault diameter in rotary part of machine is detected and classified using the machine learning methods. In the first stage, we have employed empirical mode decomposition (EMD) for high-frequency noise removal. Residue signal is obtained by removing first IMF from base signal considering first IMF as a high-frequency noisy signal, followed by wavelet decomposition. Entropy of the wavelet coefficient obtained from 3 rd level decomposition of residue signal is calculated which acts as an input parameter to the machine learning techniques to determine the diameter for fault. ree different sets have been taken for inner race, outer race, and ball race correspondingly. e proposed method classifies and detects the fault diameter up to 99.5%. e proposed method can be used for different types of continuous as well as discrete wavelets. 1. Introduction e main objective of maintenance department in industries is to keep machineries running. Machine failure makes unwanted downtime across the industries with high maintenance cost. Rolling bearing is considered as a vital component of rotating machines. Several kinds of faults exist in bearings. e faults in the bearing decrease the accuracy and performance of the machine. Bearing fault detection is done using various signal processing techniques. Various available methods to analyse the bearing fault signals are categorized in time-frequency domain [1, 2], time domain [3–5], and frequency domain [6–9]. Image processing techniques have also been explored in works for diagnosis and detection of bearing fault [10, 11]. e use of vibration signal in the analysis of bearing faults is also reported in the literature. ese vibration signals are recorded using accelerometer which may have noisy background. Kurtosis analysis is also one of the methods applied for the early-stage detection of faults in bearing. In earlier research studies, it is already proved that for kurtosis value more than three, faults are present in the vibration signal [12]. Wavelet transform (WT) is applicable for investigation of non-stationary signals. Wavelet transform is used for the decomposition of signal into approximation and detailed coefficient. Application of wavelet transforms in the de- tection of bearing faults is also reported by Kumar Jha and Swami [13]. Various statistical parameters extracted from the wavelet coefficients of vibration signals are utilized for different wavelet functions for fault detection [14]. Parey et al applied the combination of artificial Intelli- gence methods with maximum energy to Shannon entropy extracted from wavelet coefficients for the ball bearing fault classification [15]. WT has been extensively used in signal denoising. In [16], Ali Alnuaim et al suggested a procedure for rolling Hindawi Scientific Programming Volume 2022, Article ID 3428715, 10 pages https://doi.org/10.1155/2022/3428715