## 4 Proposed Fault Diagnosis Algorithm  
In order to assess the proposed intelligent diagnosis system, the main characteristics of our experiments performed on a three-phase induction machine with: Rated voltage: 415 V, Rated power: 750 W, six poles, primary rated current: 2.1 A, rated speed: 915 rpm. As shown in Figure 2a, the bench test has been set up, consisting of squirrel-cage induction motors, an AC generator, and a resistive bank, which provide four different loadings 10%, 35%, 50%, and 80% of the full load under both healthy and faulty conditions to study the effects of putting various loads in the fault identification procedure. For each condition of the motor, 20 sets of stator current signal have been collected and was sampled at 2 KHz before and after defects with a sample number of N = 2000 during the steady-state operating condition of the motor. In total, 320 sets of experiment were performed; 20 sets of motor current from four motors with different severity operating at four different load conditions. Figure 2b also demonstrates of the healthy rotor and a rotor with three broken bars. Figure 1: Three-layer structure of wavelet packet decomposition based on frequency order. Figure 2: (**a**) Experimental test rig and (**b**) Demonstration of the healthy and rotor with three broken bars. By looking at the current signal measured, we cannot realize the motor condition if broken rotor bar exists. In the word, the raw current signal does not indicate the difference between healthy and faulty conditions because the amplitude of the fault-related frequency is much smaller than fundamental frequency. As an example, typical stator current signals for a healthy motor as well as for a motor with one broken bar under 35% full load are illustrated in Figure 3. As it is clear, there is no observable difference between these two signals that can be utilized for fault (here broken rotor bar) detection. Therefore, a signal processing method is used to extract the fault-related feature for fault detection. The architecture of the intelligent diagnosis system, which is a combination of progressive signal processing and pattern recognition techniques, is given in Figure 4. This method not only improves the signal processing algorithm to find the exact fault-oriented sub-bands, but also implements a NN-based classifier to categorize the severity assessment. In the first step, the wavelet packet decomposition is applied to the monitored signal to extract features in more concentrated fault-related depths and nodes in the MATLAB(r) (2008) environment. In the next step, the reconstructed signals are transformed to frequency domain using FFT to find the optimum level and nodes of wavelet packet tree to detect fault  
Figure 3: Typical motor current signal for 35% full-load (**a**) healthy motor and (**b**) motor with one broken bar. in various loads, in particular no load condition. In addition, the statistical features extracted from wavelet packet coefficients are selected to form the classifier's input vector. At the end, the feed-forward neural network (NN) is employed to identify the BRBs severity. In this research, in order to illustrate the merits of taking FFT to follow the bands including fundamental-oriented frequency, the stator current signals are decomposed into approximations and details signal using WPD in the MATLAB(r) environment. Then, for every selected node in proceeding levels, the wavelet packet reconstructed signals are transformed to frequency domain using FFT to find the sub-band with maximum value. The FFT is used to find the optimum level and nodes are fundamental-oriented frequency as well as the fault-related band frequency to detect fault severity in various load according to Figure 5[15; 16; 44]. Consequently, regarding the closeness of broken bar fault frequency to fundamental frequency, especially in no load condition, the exact fault-related sub-band with high reliability is highlighted.