# Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
###### Abstract  
This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. E 2017172477247710.3390/s171124771247710.3390/s17112477124771247724772472477247247247724  
[PERSON] [3] proposed a technique for analyzing a defective signal from bearing vibration measurements using wavelet packet decomposition, and extraction of the dominant frequency components from the defective bearing. [PERSON] [4] decomposed the bearing signal through wavelet transforms, and characterized the standard deviation of the decomposed signal, to identify defects, by developing a neuro-fuzzy model. [PERSON] [5] conducted a study of the bearing defects of the induction motor, which is a type of AC motor. The study performed continuous wavelet transform by exploiting Morlet wavelets and Daubechies wavelets, and obtained wavelet coefficients by a support vector machine (SVM) and an artificial neural network (ANN). A method of detecting defects through time-frequency decomposition, such as the Hilbert-Huang transform (HHT), has also been proposed [6]. Moreover, techniques for decomposing signals using empirical mode decomposition (EMD), an intermediate process of HHT, have been used. [PERSON] [7; 8] also employed decomposition of the bearing signal through EMD, created the autoregressive (AR) model using the decomposed signals, and identified the type of bearing defects by separating the coefficients of the AR model. In addition, EMD demodulation technique was used to extract instantaneous frequency amplitudes, and to identify the bearing defect. [PERSON] [9] performed wavelet decomposition to extract the features due to bearing defects. The study extracted the wavelet coefficients of high levels, and reconstructed the signals. In addition, mode mixing, which is one of the side effects of the EMD, can be removed through Ensemble Empirical Mode Decomposition (EEMD). Specifically, EEMD can be used to prevent mode-mixing, so that the degradation of the damage detection performance, in which an intrinsic mode function (IMF) signal is projected onto another IMF, is reduced. [PERSON] et al. [10] selected the IMF based on the kurtosis of the signal decomposed by EEMD, and employed the wavelet neural networks (WNN) method using wavelets and artificial neural networks to detect the defects of roller bearings in railway vehicles, respectively. [PERSON] [11] introduced a damage detection and classification study using EEMD and multi-class SVM. Also, [PERSON] [12] analyzed EEMD signals of bearing system through tunable Q-factor wavelet transform (TQWT). [PERSON] [13] proposed SVM-based damage detection using EEMD and multi-scale fuzzy entropy. [PERSON] [14] applied Improved Ensemble empirical mode decomposition (IEEMD), which is an upgraded version of EEMD to tackle the noise issue, to decompose bearing signals and extract statistical features from the signals, to distinguish bearing defects using back propagation of neural networks. As can be seen from the literature, monitoring of bearing system is mostly confined to the adaptation of signal processing techniques.