**6. References**


<sup>\*</sup> Corresponding Author

Rosado L, Forster N, Thompson K, Cooke J, (2010), Rolling Contact Fatigue Life and Spall Propagation of AISI M50, M50NiL, and AISI 52100, Part 1: Experimental Results, *Tribology Transactions*, 53: 29-41.

**Chapter 0**

**Chapter 5**

**Bearing Fault Diagnosis Using Information**

Rotating machinery is very common in industrial systems, and it plays an important role in industrial development and economic development. With the rapid advancement in industry, rotating machinery is becoming more and more complex and require constant attention. Although the reliability and robustness of rotating machinery also have been improving, some occasional failure events of components often lead to unexpected downtime while resulting in huge losses. And rolling element bearing is often at the heart of these rotating machinery which suffers from fault more frequently. These faults may cause the machine to break down and decrease its level of performance [6]. So, it is urgent to diagnose the incipient errors

In traditional fault diagnosis, a single sensor is always used to get the operation conditions of several machine components. The collected signal involves many correlated features [33]. During operating process, the machine set can generate many kinds of signals. And those approaches based on the vibration signal analysis are advantageous because of their visual feature, easy measurability, high accuracy and reliability [34]. Fault diagnosis using raw vibration signals, a wide variety of techniques have been introduced in recent years. There are mainly including signal processing methods and intelligent systems application. Signal processing methods are traditional methods which are still common used, such as wavelet and wavelet packet methods [23–25], empirical mode decomposition [15, 35], time-frequency distributions [7], blind source separation [29]. While intelligent system approaches for fault diagnosis are including artificial neural networks (ANNs) [36], support vector machines (SVMs) [33], adaptive neuro-fuzzy inference system (ANFIS) [19] and fuzzy technique [28], etc.. These approaches are based on one data source or individual decision system, and many researchers have realized and shown that an individual decision system with a single data source can only acquire a limited classification capability which may not be enough for a particular application [22]. So, it is necessary to combine multiple decision systems to carry

> ©2012 Huang, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

©2012 Huang, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Fusion and Intelligent Algorithms**

Additional information is available at the end of the chapter

Jiangtao Huang

**1. Introduction**

exactly in these bearings.

on failure diagnosis.

http://dx.doi.org/10.5772/51079

