[TSA and spectra analysis (using vibration and sound signal): A Compression study to detect gear box damage]

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Acknowledgement

I would take this opportunity to thank my research supervisor, family and friends for their support and guidance without which this research would not have been possible.

DECLARATION

I, [type your full first names and surname here], declare that the contents of this dissertation/thesis represent my own unaided work, and that the dissertation/thesis has not previously been submitted for academic examination towards any qualification. Furthermore, it represents my own opinions and not necessarily those of the University.

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ABSTRACT

This paper deals with gear condition monitoring based on vibration analysis techniques. The detection and diagnostic capability of some of the most effective techniques are discussed and compared on the basis of experimental results, concerning a gear pair affected by a fatigue crack. In particular, the results of new approaches based on time-frequency and cyclostationarity analysis are compared against those obtained by means of the well accepted cepstrum analysis and amplitude and phase demodulation of meshing harmonics. Moreover, the sensitivity to fault severity is assessed by considering two different depths of the crack. The effect of choosing different transducer locations and different processing options are also shown. In the case of the experimental results considered in this paper, the power cepstrum is practically insensitive to the crack evolution. Conversely, the Spectral Correlation Density function is able to monitor the fault development and does not seem to be significantly influenced by the transducer position. The demodulation techniques are able to localise the damaged tooth; however, their sensitivity is strongly dependent on the proper choice of the filtering band and is affected by the transducer location. The Wavelet transform seems to be a good tool for crack detection; it is particularly effective if the residual part of the time synchronous averaged signal is processed.

Table of Content

ACKNOWLEDGEMENTII

DECLARATIONIII

ABSTRACTIV

CHAPTER 1: INTRODUCTION1

CHAPTER 2: LITERATURE REVIEW3

Time Synchronous Averaging3

Joint Time-Frequency Analysis Techniques5

Short Time Fourier Transform (STFT)7

Wigner-Ville Distribution (WVD)8

Vibration9

Sound11

Stereophonic sounds13

Sound Propagation14

Sound Analysis16

Vibration, repetition, generation18

Spacing, surfacing, shaping21

Transmission, diffusion, absorption23

Vibration Analysis Health Monitoring Software27

Mobius Ilearn Interactive28

CHAPTER 3: METHODOLOGY29

Processing Issue29

Preprocessing Issues29

Feature Descriptions31

Experimental Data32

Tests33

Analysis: Data Set35

CHAPTER 4: DISCUSSION AND ANALYSIS36

TSA Effects37

Cepstrum analysis40

Demodulation analysis42

Cyclostationary analysis45

Wavelet transform47

Sideband Removal Effects49

Optimal Parameters50

CHAPTER 5: CONCLUSION52

REFERENCES53

LIST OF FIGURES58

CHAPTER 1: INTRODUCTION

A vibration FFT (Fast Fourier Transform)spectrum is an incredibly useful tool for machinery vibration analysis. If a machinery problem exists, FFT spectra provideinformation to help determine the source andcause of the problem and, with trending, howlong until the problem becomes critical.FFT spectra allow us to analyze vibrationamplitudes at various component frequencieson the FFT spectrum. In this way, we canidentify and track vibration occurring atspecific frequencies. Since we know that particular machinery problems generatevibration at specific frequencies, we can usethis information to diagnose the cause of excessive vibration. Most modern techniques for gear diagnostics are based on the analysis of vibration signals picked up from the gearbox casing. The common target is to detect the presence and the type of fault at an early stage of development and to monitor its evolution, in order to estimate the machine's residual ...