Advanced System for Automatically Detecting Faults Occurring in Bearings
Published on 15. February 2021
92 pages
978-1-61761-238-1 (ISBN)
Description
Maintenance is essential in all kinds of machinery. In order to prevent long-term breakdown or catastrophic failure, faults should be detected at their incipient stage. Bearing faults are the most frequent cause of failures of machinery. Most of the faults occurring in bearings will introduce impact-like vibration signals when bearings are rotating. Such impacts behave like damped oscillations and can be detected using accelerometers. This book introduces an automatic, effective but simpler-to-use system for bearing fault diagnosis so that the industry can use its equipment without the need of hiring experts.
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Peter W. Tse | Jacko C. Leung
Advanced System for Automatically Detecting Faults Occurring in Bearings
Book
03/2011
Nova Science Publishers Inc
€96.74
Shipment within 15-20 days
Content
- Intro
- Library of Congress Cataloging-in-Publication Data
- Contents
- Preface
- Introduction
- 1.1. The Background of Machinery Fault Detection
- 1.2. The Motivation on Developing an Automatic System for Detecting Bearing Faults
- 1.3. The Review of the Maintenance Approaches
- 1.3.1. The Run-to-Breakdown Approach
- 1.3.2. The Time-Based Preventive Maintenance Approach
- 1.3.3. The Condition-Based Maintenance Approach
- 1.3.4. The Advantages for Adopting the Conditional-Based Maintenance Approach
- The Review of the Machinery Fault Detection Methodology
- 2.1. Transducer Used for Measuring Vibration Signal
- 2.2. Vibration Trend
- 2.3. The Introduction of Fast Fourier Transform (FFT)
- 2.3.1. The Principle of FFT
- Definition of FFT
- 2.3.2. The Limitation of Existing FFT
- 2.4. The Introduction of Wavelet Transform
- 2.4.1. The Principle of Wavelet Transform
- 2.4.2. The Limitation of Existing Wavelet Transform
- 2.5. The introduction of the Reassignment Wavelet Transform
- 2.5.1. The Principle of Reassignment
- 2.5.2. The Definition of Reassignment Wavelet Transform
- 2.6. The Use of High Order Statistics in Machine Fault Detection
- 2.6.1. Kurtosis
- 2.6.2. Spectral Kurtosis
- 2.6.3. Root Mean Square (RMS)
- 2.6.4. The Rationale of Proposing a Novel Spectral RMS X Kurtosis for Effective Bearing Fault Detection
- 2.6.5. Spectral RMS x Kurtosis
- 2.7. The Evolution of Reassignment Wavelet Based Spectrum RMS X Kurtosis
- The Design of the Virtual Based Automatic Fault Detection System
- 3.1. Development of a Single Tasked Data Acquisition Program
- 3.2. Implementation of the Reassignment Wavelet Analysis
- 3.3. Screen Flow Design and Functionality
- 3.4. Time Domain Analysis
- 3.5. Data Storage and Extraction
- 3.6. The Layout of the Hardware Configuration
- Experiment on Laboratory Machinery Fault Simulator
- 4.1. The Bearing Fault Simulator
- 4.2. The Rolling Element Bearings
- 4.3. Artificially Induced Bearing Defects
- 4.4. A Comparison Study of Conventional CWT and Reassignment Wavelet
- 4.5. Bearing Fault Detection by
- Using Spectral RMS x Kurtosis
- 4.6. Detection of a Normal Condition Signal and Motor Signature
- 4.7. The Analysis of the Bearing Signal Collected from a Bearing with a Ball Defect
- 4.8. The Analysis of Bearing Signals Collected from Bearings with Outer Race and Inner Race Defects
- 4.9. Experimental Analysis with Computer Generated Noise Simulation
- Experiments on Industrial Machines
- 5.1. The Analysis of the Bearing Signal Collected from a Railway Traction Motor
- 5.2. The Industrial Cooling Water Pump
- 5.3. Automatic Signal Processing Using a Virtual Instrument
- Discussion
- Conclusions
- Acknowledgments
- References
- Index
