# Optimal Intrinsic Mode Function Based Detection of Motor Bearing Damages
## 1 Introduction  
Motors are widely applied as reliable pieces of equipment in many industries. However, unwanted motor failure could happen due to several factors: improper operation, manufacturing defects and/or mechanical wear [1; 2]. According to literature [3; 4], the most possible motor failure event is bearing damages. Understanding the signals of bearing damages could prevent unwanted motor failure events [5; 6; 7].  
Classical signal analysis such as fast Fourier transforms (FFT) [8] and wavelet transform (WT) [9; 10; 11] have been widely applied in realizing the problem of abnormal bearings. However, the disadvantages include that FFT is constrained by limited basis, and WT encounters difficulty in mother wavelet selection. These existing disadvantages of the two methods sometimes result in the distortion of signal analysis results.  
Recently, Hilbert-Huang transform (HHT) [12; 13; 14], composed of empirical mode decomposition (EMD) [15; 16], and Hilbert transform (HT) [17; 18] have been widely applied to analyze nonlinear and non-stationary signals. EMD decomposes measured signals to several layers of intrinsic mode functions (IMFs), but EMD also decomposes noise signals into IMFs. Therefore, it is important to select some IMFs not affected by noise to avoid the noise interference.  
Particle swarm optimization (PSO), ant colony optimization (ACO), and bee colony optimization (BCO) are discussed in search optimal solution [19; 20; 21]. However, dimensional search-spaces of these algorithms are high for complicated problems. Using these optimization models for problems, the convergence parameters need to be determined by specific empiricism, which need extra computationaltime to obtain the optimal solution. The superiority of the greedy algorithm (GA) [22; 23] to select optimal IMFs is simple and easy-to-implement to complicated or/and multi-step problems. Therefore, the paper proposes a model which applies the GA to select the optimal IMFs of EMD, namely greedy empirical mode decomposition (GEMD), to simplify the bearing damage detection system.  
Compared to other artificial neural networks (ANNs), such as feed-forward network [24; 25] or probabilistic neural network (PNN) [26; 27], back propagation neural network (BPNN) [28; 29] is a supervised neural network in which signals will be trained in advance to obtain the network weight parameters. The detection accuracy of BPNN is superior to that of other ANN models. Therefore, the BPNN is adopted in the detection system based on the proposed GEMD model, and the detection accuracy in noise interference environments will validate that the GEMD model is more suitable for the application to detect bearing damages.  
This paper first introduces the HHT and the proposed GEMD models, and then motors' current signals of different types of bearing damages were measured and analyzed. The modular structure serves to detect the bearing damages as subjected to damaged bearings such as in Figure 1. EMD decomposes the measured current signals to a number of layers of IMFs, and then the optimal IMFs by using the proposed GEMD can be obtained. The HHT spectrum of the optimal IMFs transformed by HT can be used to observe the damage characteristics for the alarm system. Compared with the classical HHT method, the proposed GEMD model renders the current signal analysis as a refined HHT spectrum. Alternatively, the features of various damage types can be extracted from the HHT spectrums, and the superior recognition accuracy by using the BPNN model can be validated.