**1. Introduction**

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Large-scale and complex mechanical equipments usually operate under complicated and terrible conditions such as heavy duty, erosion, high temperature, etc. Therefore, it is inevitable for the key components (bearings, gears and shafts, etc.) of these equipments to suffer faults with various modes and different severity degrees. However, faults of largescale and complex mechanical equipments are characterized by weak response, multi-fault coupling, etc., and it is hard to detect and diagnose incipient and compound faults for these equipments.

One of the principal tools for diagnosing mechanical faults is vibration-based analysis [1– 3]. Through the use of processing techniques of vibration signals, it is possible to obtain vital diagnosis information from the signals [4, 5]. Traditional fault diagnosis techniques are performed by diagnosticians observing the vibration signals and the spectra using their expertise and special knowledge. However, for mechanical equipments having complex structures, many monitoring cells and high degrees of automation, there is lots of data to be analyzed in the process of fault diagnosis. Obviously, it is impossible for diagnosticians to manually analyze so many data. Thus, the degree of automation and intelligence of fault diagnosis should be enhanced [6]. Researchers have applied artificial intelligent techniques to fault diagnosis of mechanical equipments, such as expert systems, fuzzy logic, neural networks, genetic algorithms, etc [7–10]. Correspondingly, prominent achievements have been obtained in the field of intelligent fault diagnosis. With the advancement of studies and applications, however, researchers find that individual intelligent techniques have their advantages and shortcomings as well. For incipient and compound faults of mechanical equipments, the diagnosis accuracy using an individual intelligent technique is quite low and the generalization ability is considerably weak. Thus, it is urgent and necessary to develop novel techniques and methods to solve these problems.

The Hybrid Intelligent Method Based on

Fuzzy Inference System and Its Application to Fault Diagnosis 155

Fig. 1. Flow chart of the hybrid intelligent fault diagnosis method

Twenty-four feature parameters (*p*1–*p*24), presented in Table 1, are extracted [6] in this study. Eleven parameters (*p*1–*p*11) are time-domain statistical features and thirteen parameters (*p*12– *p*24) are frequency-domain ones. Once faults occur in mechanical equipments, the timedomain signal may change. Both its amplitude and distribution will be different from those of signals collected under healthy conditions. In addition, the frequency spectra and its distribution may change as well, which indicates that new frequency components appear and the convergence of frequency spectra varies. Parameter *p*1 and *p*3–*p*5 reflect the vibration amplitude and energy in time domain. Parameter *p*2 and *p*6–*p*11 represent the time series distribution of the signal in time domain. Parameter *p*12 indicates the vibration energy in frequency domain. Parameter *p*13–*p*15, *p*17 and *p*21–*p*24 describe the convergence of the spectrum power. Parameter *p*16 and *p*18–*p*20 show the position change of major frequencies.

**2.1 Feature extraction** 

**2.1.1 Statistical features of raw signals** 

The combination of multiple intelligent techniques has been intensively studied to overcome the limitations of individual intelligent techniques and achieve better performance [11–13]. Multiple intelligent classifiers input different feature sets usually exhibit complementary classification behaviors. Thus, if the classification results of multiple intelligent techniques are combined to yield the final classification result, the final performance may be superior to the best performance of individual classifiers [14–16].

Based on the above analysis, a hybrid intelligent fault diagnosis method is presented in this chapter to diagnose incipient and compound faults of complex equipments. The method is developed by combining multiple adaptive neuro-fuzzy inference systems (ANFISs), genetic algorithms (GAs) and vibration signal processing techniques. The method employs signal preprocessing techniques to mine fault information embedded in vibration signals. Statistical features reflecting machinery health conditions from various aspects are synthesized to construct multiple feature sets and to fully reveal fault characteristics. Using an improved distance evaluation technique, the sensitive features are selected from all feature sets. Based on the independency and the complementary in nature of multiple ANFISs with different input feature sets, a hybrid intelligent method is constructed using GAs. The hybrid intelligent method is applied to fault diagnosis of rolling element bearings. The experimental results show the method based on multiple fuzzy inference systems is able to reliably recognize both incipient faults and compound faults.

The rest of this chapter is organized as follows. Section 2 presents the hybrid intelligent method based on fuzzy inference system. Feature extraction and selection, and adaptive neuro-fuzzy inference system are also introduced in this section. Section 3 gives two cases of fault diagnosis for rolling element bearings. The hybrid intelligent method is applied to diagnosing bearing faults and the corresponding results are reported. Section 4 compares the proposed hybrid intelligent method with individual intelligent methods in the light of classification accuracy, and further discusses the causes of the improvement produced by the hybrid method. Finally, conclusions are given in Section 5.
