Description
ABSTRACT
Vibration signals resulting from rolling element bearing in a machine imply important bearing defect information related to the machinery faults. Any defect in a bearing may cause a certain vibration with specific frequencies and amplitudes depending on the nature of the defect. Therefore, the vibration analysis plays a key role for fault detection, diagnosis, and prognosis to reach the reliability of the machines. Although fast Fourier transform for time–frequency analysis is still widely used in industry, it cannot extract enough frequencies with-out enough samples. If the real frequency does not match fast Fourier transform frequency grid exactly, the spectrum is spreading mostly among neighboring frequency bins. To resolve this drawback, the recent proposed enhanced fastFouriertransformalgorithmwasreportedtoimprovethissituation.ThisarticlereviewsandcomparesbothfastFouriertransformandenhancedfastFouriertransformforvibrationsignalanalysisinbothsimulationandpracticalwork.Thecomparative results verify that the enhanced fast Fourier transform can provide a better solution than traditional fast Fourier transform.
TABLE OF CONTENTS
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
- STATEMENT OF THE PROBLEM
- PURPOSE OF THE STUDY
- SIGNIFICANCE OF THE STUDY
CHAPTER TWO
LITERATURE REVIEW
- OVERVIEW OF FAST FOURIER TRANSFORM
- APPLICATION OF FFT
- VARIOUS TECHNIQUES FOR SIGNAL ANALYSIS
- EXPERIMENTAL PROCEDURE AND VIBRATION SIGNAL ANALYSIS
- CONCLUSION AND RECOMMENDATION
REFERENCES
CHAPTER ONE
1.0 INTRODUCTION
1.1 BACKGROUND OF THE STUDY
Rolling element bearings have been widely applied in domestic and industrial machinery. These bearings are considered as most critical components, and defects in bearings may causal function or even lead to serious failure of the machinery during operation. The health condition and quality inspection of bearings are directly related to these defects(Ben et al., 2015). Therefore, the industrial vibration analysis is regarded as an important measurement tool for identification,prediction, and prevention of failures in rotating machinery.For this reason,implementing vibration analysis on the machines can improve the machine efficiency and reliability. Usually, the measurement vibration involves accelerators to measure the vibration, and then, the data can be collected for further analysis. The plots of vibration signal with time domain or frequency domain may provide sufficient information for the engineers to analyze and determine the machine fault. Many bearings’ premature function may occur from surface roughness, misalignment,discrete defects,unbalance,contamination and temperature extreme, and geometric alim perfections. Modern machines may produce the vibration frequency range between 20 Hz and 20 kHz (Ben et al., 2015). When a fault of rolling bearings begins to develop, the resultingvibrationpulses’frequenciesmayrepeatperiodically.Aband of high-frequency vibration may therefore exist be fore a rolling-element bearing is burnt out.
The fast Fourier transform (FFT) presents a more efficient computation process. In practice, however, thelimitationoftheFFTmakesitlessefficientinanalyzingthesignalspectrumfromdefectiverollingelementbearingsduetocyclostationaryandnon-stationary characteristics.
1.2 STATEMENT OF THE PROBLEM
Present day requirements for enhanced reliability of rotating equipment are most critical than ever before, and demands continue to grow constantly. Detection of faults play important role in the quest for highly reliable operations. Reducing maintenance and production cost, improving up time, product quality, advance safety and reducing risks are some of the essential drivers for deploying vibration analysis. These serve as goals of any plant or corporation. Vibration analysis for predictive maintenance is an important ingredient in all these goals.
1.3 PURPOSE OF THE STUDY
The purpose of this work is to analyze the vibration signals of electrical rotating machines and diagnoses the health of machine for predictive maintenance requirements using Fast Fourier Transform (FFT).
This work also aims at overcoming the limitations of traditional vibration analysis techniques.
1.4 SIGNIFICANCE OF THE STUDY
The Vibration analysis of electrical rotating machines lies on the fact that all rotating machines in good condition have fairly stable vibration pattern. Under any abnormal condition in working of machines, the vibration pattern gets changed. The amount of variation can be detected and the nature of abnormalities can be analyzed.
The FFT helps engineers determine the excitation frequencies in a complex signal and their amplitude. It also highlights changes in frequency and amplitude and harmonic excitation in the selected frequency range.
2.5 Conclusion
The vibration analysis of a rotor-bearing system has been demonstrated through FFT using the test rig signal. The vibration signals were measured at a constant shaft rotating speed of 3000 rpm. Following vibration features are captured in the present work.
- It has been observed that level 2 decomposition of the vibration signal performed using theempiricalmodedecompositionmethodishavinghighestvibrationlevelatshaftrunningfrequency.
