**2. Bearing fault diagnosis using fractal feature parameter classification**

Faulty and normal machine conditions are always treated as classification problems based on learning pattern from empirical data modeling in complex mechanical processes and systems [31]. In this approach, a general framework for applying classification methods to fault diagnosis problems includes two steps: representative feature extraction and pattern classification. Feature extraction is a mapping process from the measured signal space to the feature space. Representative features which demonstrate the information of fault are extracted from the feature space. Pattern classification is the process of classifying the extracted features into different categories by geometric, statistic, neural or fuzzy classifiers. And recently, the development of artificial intelligence techniques has led to their application in fault diagnosis area. Meanwhile, artificial neural networks (ANNs) and support vector machines (SVMs) have been successfully applied to the intelligent fault diagnosis of mechanical equipment [27].

In practice, the classical approach is not always reliable when the extracted features are contaminated by noise. And most intelligent fault diagnosis approaches are complex, especially in solving multiple fault diagnosis problems. In this section, a novel, simple, fast and reliable intelligent method for solving multiple fault diagnosis problem will be proposed. And this approach is based on EMD and fractal feature parameter extraction.
