**8. References**


**8** 

**The Hybrid Intelligent Method Based on** 

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

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

**1. Introduction** 

equipments.

methods to solve these problems.

**Fuzzy Inference System and** 

*State Key Laboratory for Manufacturing Systems Engineering,* 

**Its Application to** 

**Fault Diagnosis** 

*Xi'an Jiaotong University,* 

Yaguo Lei

*Xi'an China* 

