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Ball bearing is used to provide free rotation around a fixed axis. Various kinds of faults exist in the bearing such as inner race fault, outer race fault and ball race fault. One very effective method to diagnose the bearing fault is vibration signal analysis. Empirical mode decomposition (EMD) has been used for ball-bearing fault diagnosis in mechanical systems using vibration signal analysis. Classification of the ball-bearing fault has always been a challenging task. Various classification schemes such as Extreme learning machine (ELM), K-means Clustering, and Support vector machine (SVM), have been reported in the literature for ball-bearing fault classification. SVM is restricted by multiclass classification efficiency, and ELM is restricted by the longer training. In this paper, the entropy analysis of the wavelet coefficient obtained from the third level decomposition of the residue signal (obtained after subtracting the highest frequency component from the raw signal) has been done for ball-bearing fault classification. A comparative analysis of the wavelet coefficient based on entropy measurement has been presented here. High fault classification accuracy has been achieved using the proposed method for the detection of ball-bearing fault. Shannon entropy, Average Shannon entropy, and Renyi's entropy are parameters for the justification of the proposed approach. The result shows the best wavelet to be chosen among the available discrete wavelet based on various entropy measurements. [ABSTRACT FROM AUTHOR]

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 3rd level decomposition of residue signal is calculated which acts as an input parameter to the machine learning techniques to determine the diameter for fault. Three different sets have been taken for inner race, outer race, and ball race correspondingly. The proposed method classifies and detects the fault diameter up to 99.5%. The proposed method can be used for different types of continuous as well as discrete wavelets. [ABSTRACT FROM AUTHOR]

Ball bearings are considered as a critical element in various mechanical systems. Vibration signal analysis is very effective method for finding bearing fault. Accelerometers are used to capture the multi-component vibration signal generated in the machine when it is in use. Various methods based on empirical mode decomposition (EMD) have been used for ball bearing fault diagnosis. EMD method usually suffered from the boundary distortion of intrinsic mode function. Classification of ball bearing fault is one of the challenging tasks in the field of mechanical systems. Various classification schemes such as support vector machine (SVM), K-means clustering, extreme learning machine (ELM) have been used for the classification of ball bearing fault. In this study, footprint analysis of Hilbert transform along with the neural network has been done for ball bearing fault analysis. A comparative analysis of the proposed research study has been done with available methods such as SVM and ELM. A high fault classification accuracy has been achieved using the proposed method for detection of ball bearing fault. [ABSTRACT FROM AUTHOR]

• This paper presents a new method based on EMD for classifying muscle diseases from electromyogram signals. • In the proposed method, the suitable IMF for feature selection is determined using the t-test based approach. • The two visual features namely area (A) and circumference (CF) are computed using the complex plane plot of the IMF. • The proposed algorithm has been trained and validated using a feed forward neural network, SVM, and decision tree. • The proposed method is given better performance compare to exiting methods in terms of performace parameter. Muscle activity decreases due to various conditions like age factors and muscle diseases namely, amyotrophic lateral sclerosis (ALS) and myopathy. Electromyogram (EMG) signals are regularly explored by specialists to analyze the irregularity of muscles. Manual investigation of EMG signals is a tedious task for medical practitioners. Therefore, this work proposes a new method for classifying the ALS, myopathy, and normal EMG signals. In the proposed method, the empirical mode decomposition (EMD) method is applied to decompose the EMG signals into intrinsic mode functions (IMFs). The suitable IMFs for feature selection are selected using the t-test based approach and used to compute the foot distances denoted as fp 1 and fp 2 by constructing the complex plane plot. The quadrilateral is drawn over a complex plot by considering fp 1 and fp 2 as a diagonal of it, followed by calculating the area (A) and circumference (CF) of the quadrilateral. These measures are utilized for separating the three classes of myopathy, ALS, and normal EMG signals. The proposed algorithm has been trained and validated using a feed forward neural network (FFNN), support vector machine (SVM), and decision tree. The algorithm, when tested with a FFNN, achieved the maximum classification accuracy, sensitivity, and specificity of 99.53%, 99.25% and 99.60%, respectively. [ABSTRACT FROM AUTHOR]