## 2 Components of the Generator of Expert Systems for Fault Diagnosis of IMs
### Methods to Obtain Fault Representative Features  
Under steady-state regime, the stator current analysis is performed using the frequency spectrum of the stator current, via the FFT [14]. Nevertheless, in many modern industrial processes, IMs operate under transient conditions (due to varying loads, action of controllers, etc.). The methods used for obtaining fault representative features and analyzing them in steady-state regime are usually not valid in transient state (for example, the FFT cannot be used under varying speed conditions). On the contrary, most methods designed for transient state diagnosis are also valid for the detection of failures in steady-state condition, but at the cost of a higher computational complexity both of the set of diagnostic features and of the analysis algorithms. For these reasons, this research focuses on the fault diagnosis both in steady-state regime and in transient state.  
Recently, the development of diagnostic techniques focused on machines working in transient regimes have attracted the attention of many researches, giving raise to works as [15; 16; 17] dealing with this subject. In [15] the empirical mode decomposition (EMD) is used to obtain the fault features. The proposed approach, to obtain the fault features not only needs to compute the consecutive intrinsic mode functions (IMFs) containing the fault related components, but also the average value and the zero crossing of the sum of the IMFs. This process adds disturbances losing information about the fault evolution. In [16] the discrete wavelet transform (DWT) is used to decompose the Park vector of the stator currents in 12 levels; this approach implies a dyadic decomposition of the frequency bands and a computation of the energy contained in each range. Therefore, the information used (the energy of frequency band) is not only related to the fault features but also to other components or effects such as the spectral leakage that can significantly influence on the results. In a second step, the standard deviation of this data is used as fault features being less precise. Therefore, the use of a self-organizing map (SOM) network is proposed to detect the presence or absence of a given fault.  
In [17] the time-frequency plane is used to detect the fault. Nevertheless, the proposed method is only valid for the detection of the rotor broken bar fault in induction motors and only for the start-up transient. The fault feature is a complex representation of the characteristic V-shape pattern (with specific width and angle) for this fault (rotor broken bar) and this regime (start-up transient). It requires complex image treatment (dilatations, erosions, thresholds, subtraction of images, etc.) to obtain the fault features and implies a loss of fault information and a very limited field of application. Therefore, although these techniques can be also applied to obtain the fault features in steady-state regimes theses fault features would be more complex and less precise than the specific methods for steady state regimes.  
A recent development in fault diagnosis of IMs allows to apply the same tool for analyzing the spectral content of the motor current in both regimes. This technique, known as harmonic order tracking analysis (HOTA) [4; 5; 18] allows to design a unique expert system for both regimes. Therefore, it is chosen as the basis of the diagnostic system generator proposed in this paper.