Application Of Wavelet Analysis In Fault Diagnosis

The techniques for fault diagnosis are under rapid development. This is due to the interlacing between the demands from the practical applications and the achievements in various fields of the theory and the technology. As the modern technology develops, the engineering control systems become more and more complicated and advanced in practical applications, therefore the reliability and the security of those systems will play a critical role to both the society and the economy. More and more attention has been paid in the reliability and security in the last two decades. From the academic view, fault diagnosis has a closer relationship with many areas on science and technology such as modern control theory, signal processing, pattern recognition and artificial intelligence. The rapid development and the increasing achievements on these areas during the past two decades lay a solid foundation for the fault diagnosis of complicated systems.Wavelet analysis is a novel mathematical theory and method proposed in 1980s. It is regarded as a breakthrough of Fourier analysis because it has many wonderful characteristics. Fourier analysis consists of breaking up a signal into sine waves of various frequencies. Similarly, wavelet analysis is the breaking-up of a signal into shifted and scaled versions of the original wavelet. Lacking of space locality in time domain, Fourier analysis can only make certain of the integral singularity of a function or signal. As a result, it is difficult to detect the spatial position and distribution of broken signal by Fourier analysis. Wavelet analysis has the characteristic of spatial locality, and its wideness in both windows of the time and the frequency can be adjusted, so it can analyze the details of a signal. Therefore Wavelet analysis is called a microscope of signal analysis and a milestone of Fourier analysis. As a new embranchment of mathematics, Wavelet analysis is the most perfect combination of the functional analysis, Fourier analysis and numerical analysis. Wavelet analysis has been widely used in signal processing, image manipulation, voice analysis, pattern recognition, quantaphysics, biomedicine engineering, computer vision, fault diagnosis, some nonlinear fields and so on.In this dissertation, the fault diagnosis techniques based on Wavelet analysis were investigated. The relationship between the wavelet coefficient modules and the broken signal, the multiple-level Wavelet decomposition and reconstruction for de-noising, and the Wavelet neural network were analyzed or studied and were applied to detect the variant types of faults. The dissertation is outlined below.In Chapter 1 we will briefly introduce the state-of-art of fault diagnosis. The major aspects on its theory and techniques will be summarized. The applications of the Wavelet analysis in fault diagnosis will also be introduced.Chapter 2 is devoted to compare Wavelet analysis and Fourier analysis. The features of each element in Wavelet family will be studied, and the criteria in choosing the wavelet function for different purposes will be outlined. An example is given to show how to use the relationship between the wavelet coefficient modules and the broken signal to achieve fault diagnosis.In Chapter 3 the multiple-level Wavelet decomposition and reconstruction for de-noising will be investigated and applied to detect faults. A practical example will be given to show the fault diagnosis procedure.Chapter 4 will be on the application of the Wavelet Neural Network (WNN) in fault diagnosis. The basic knowledge on artificial neural network and its application in fault diagnosis will be introduced. The structure and the algorithm of the WNN will be studied. A realization model for WNN will be constructed.In Chapter 5, the WNN is applied to build up a fault diagnosis system for the cooling system of the marine engine.Chapter 6 will give a summary of this dissertation. Also we will put forward some problems to be studied further and trends of development in this field.