**1. Introduction**

312 Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

Wu, J.; Hsu, C.; Wu, G. (2009). Fault gear identification and classification using discrete

Xian, G.; Zeng, B. (2009). An intelligent fault diagnosis method based on wavelet packet

Xian, G. (2010). Mechanical failure classification for spherical roller bearing of hydraulic

Xu, Q.; Li, Z. (2007). Recognition of wear mode using multi-variable synthesis approach

Yan, R.; Gao, R.X. (2005). An efficient approach to machine health diagnosis based on

Yan, R.; Gao, R. (2010). Harmonic wavelet-based data filtering for enhanced machine defect

YanPing, Z.; ShuHong, H.; JingHong, H.; Tao, S.; Wei, L. (2006). Continuous wavelet grey

Yuan, J.; He, Z.; Zi, Y.; Lei, Y.; Li, Z. (2009). Adaptive multi-wavelets via two-scale similarity

Yuan, J.; He, Z.; Zi, Y. (2010). Gear fault detection using customized multiwavelet lifting

Zarei, J.; Poshtan, J. (2007). Bearing fault detection using wavelet packet transform of

Zhou, R.; Bao, W.; Li, N.; Huang, X.; Yu, D. (2010). Mechanical equipment fault diagnosis

Zhu, Z.K.; Yan, R.; Luo, L.; Feng, Z.H.; Kong, F.R. (2009). Detection of signal transients based

*Applications*, Vol.36, No.3, Part 2, (April 2009), pp. 6244-6255

*Signal Processing*, Vol.21, No.8, (November 2007), pp. 3146-3166

*and Signal Processing*, Vol.20, No.5, (July 2006), pp. 1202-1220

Vol.36, No.10, (December 2009), pp. 12131-12136

Vol.21, No.4-5, (August-October 2005), pp. 291–301

*Processing*, Vol.23, No.5, (July 2009), pp. 1490-1508

*Processing*, Vol.20, No.1, (January 2010), pp. 276-288

*Processing*, Vol.23, No.4, (May 2009), pp. 1076-1097

Vol.37, No.10, (October 2010), pp. 6742-6747

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pp. 763-769

wavelet transform and adaptive neuro-fuzzy inference, *Expert Systems with* 

analysis and hybrid support vector machines, *Expert Systems with Applications*,

injection molding machine using DWT–SVM, *Expert Systems with Applications*,

based on wavelet packet and improved three-line method, *Mechanical Systems and* 

harmonic wavelet packet transform, *Robotics and Computer-Integrated Manufacturing*,

identification, *Journal of Sound and Vibration*, Vol.329, No.15, (July 2010), pp. 3203-

moment approach for vibration analysis of rotating machinery, *Mechanical Systems* 

transforms for rotating machinery fault diagnosis, *Mechanical Systems and Signal* 

schemes, *Mechanical Systems and Signal Processing*, Vol.24, No.5, (July 2010), pp.

induction motor stator current, *Tribology International*, Vol.40, Issue 5, (May 2007),

based on redundant second generation wavelet packet transform, *Digital Signal* 

on wavelet and statistics for machine fault diagnosis, *Mechanical Systems and Signal* 

The manufacturing productivity can be achieved through the availability of the physical resources and improved manufacturing methods and technology. The operational availability of various industrial systems can be increased by adopting efficient maintenance strategies. An ideal maintenance strategy meets the requirements of machine availability and operational safety at minimum cost.

Today, most maintenance actions are carried out by either corrective (run to failure) or preventive (scheduled or predetermined) strategy. In Corrective Maintenance (CM) the components are maintained after obvious faults or actual breakdown has occurred. With this maintenance strategy the associated costs are usually high due to the production losses, fault occurrence damages, restoring equipment until is being used at failure condition, and the safety/health hazards presented by the fault. However, the Preventive Maintenance (PM) approach has been developed to overcome the CM deficiencies. Traditionally, PM is a time driven process which is performed at regular time intervals, commonly termed the maintenance cycle, regardless of the components actual condition, in order to prevent component or systems breakdown. For example, changing the car engine oil at every 5000 KMs traveled distance, where no concern as to the actual condition and performance capability of the replaced oil.

Over recent decades some industries have started to employ a second type of PM actions in a predictive manner, where the actual machinery condition is the key indicator for the maintenance schedule and appropriate maintenance tasks (condition driven), therefore referred to as Condition Based Maintenance (CBM).

In CBM systems, the machinery condition assessment is achieved by acquiring and interpreting the actual machine data continuously with an aim to provide lead-time and required maintenance prior to predicted failure or loss of efficiency (Just-In-Time maintenance). The application of the CBM approach provides the ability to optimize the availability of process machinery, and greatly reduce the cost of maintenance. The CBM system also provides the means to improve product quality, productivity, profitability, safety and overall effectiveness of manufacturing and production plant.

The tools and techniques employed in the field of the CBM systems include: measurement and sensor technology, modeling of failure mechanisms, failure forecasting techniques,

Wavelet Analysis and Neural Networks for Bearing Fault Diagnosis 315

Bearings permit a smooth low friction motion between two surfaces (usually a shaft and housing) loaded against each other. The terms rolling-contact bearing, antifriction bearing, and rolling bearing are all used to describe that class of bearing in which the main load is transferred through elements in rolling contact rather than in sliding contact (sliding bearings). The basic concept of the rolling element bearing is simple. If loads are to be transmitted between surfaces in relative motion in a machine, the action can be achieved in the most effective way if the rolling elements are interposed between the sliding members. The frictional resistance encountered in sliding is then largely replaced by much smaller resistance associated with rolling, although this arrangement is accompanied with high

The standard configuration of a rolling element bearing is an assembly of the outer and inner rings which enclose the rolling elements such as balls (ball bearings), Figure 1a, and cylindrical rollers (roller bearings), Figure 1b, and the cage or separator which assures annular equidistance between the rolling elements and prevents undesired contacts and rubbing friction among them. Some bearings also have seals as integrated components.

> (a) (b)

contact ball bearing, and (d) thrust bearing (Harris, 2001).

(c) (d)

Fig. 1. Rolling element bearing (a) deep groove ball bearing, (b) roller bearing (c) angular

**2. Rolling element bearings** 

Outer-race

Inner-race

Cage

stresses in the contact regions of effective load transmission.

Rolling element (ball)

diagnostic and prognostic software, communication protocols, maintenance software applications and computer networking technologies.

The concept of condition monitoring consists of a selection of measurable parameters which correlate with the health or condition of a machine, and an interpretation of the collected data to determine the machinery fault existence and identify specific components (e.g. gear set, bearings) in the machine that are degrading, *Detection mode*. Moreover, the condition monitoring activities may include: specify the component failure causes, *Diagnostic mode*, and estimate the remaining life of the monitored component, *Prognostic mod*e. For example, the particles content in the lubricant oil is an indicator of the machine's wearing condition. By setting warning limits for the particles content of the lubricant a preventive action can be taken before the catastrophic failure occurs. With more detailed analysis of the measurement the nature of the problem can be identified, and lead to the diagnosis of the problem. The level of automation in assessing the machine condition can vary from human visual inspection to fully automated systems with sensors, data manipulation, condition monitoring, diagnosis, and prognosis.

Various parameters e.g. vibration, temperature, lubricant oil analysis, thermography, electric current, acoustic emission, etc, and different data analysis techniques have been applied and developed to provide significant data analysis for CM, which include:

*Time domain methods*: using different statistical indicators such as, Root Mean Square (RMS), Peak value, Kurtosis, etc. (Orhan et al. .2006) and (Tandon , 1994).

*Frequency domain methods*: such as Fourier Transform (FT) spectrum (Reeves, 1994), envelope detection (Weller, 2004), Cepstrum, etc.

*Time-Frequency methods*: which include Short Time Fourier Transform (STFT) (Thanagasundram and Schlindwein, 2006), Wavelet Analysis (WA) (Peng and Chu, 2004) (Wang and Gao , 2003), (Junsheng *et al.* , 2007 ) and (Kahaei et al. , 2006), etc.

*Adaptive noise cancellation methods*: such as Adaptive Noise Canceling (ANC), and Adaptive Line Enhancer (ALE), etc. (Khemili and Chouchane, 2005)

Bearing failures represent a high percentage of the breakdowns in the rotating machinery and result in serious problems, mainly in places where machines are rotating at constant and high speeds, not only because of the large quantity of them installed in rotating machinery, but also due to their role in relation to product quality.

This chapter presents the application of wavelet analysis combined with artificial neural networks as an automatic rolling bearing fault detection and diagnosis, with applied to both simulated (modeling) and real (measured) bearing vibration signals.

The chapter has been divided into two parts, in the first part the application of the wavelet analysis as a bearing fault detection/diagnosis technique is presented. The wavelet fault detection techniques are based on the use of the autocorrelation of the wavelet de-noised vibration signal and the wavelet envelope power spectrums for the identification of bearing fault frequencies.

The second part includes the application of wavelet analysis as a feature extraction method combined with the neural network classifier for automatic detection and diagnosis of the rolling bearing fault.
