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Author: Babak Eftekharnejad Publisher: ISBN: Category : Languages : en Pages :
Book Description
Acoustic emission (AE) is one of many technologies for health monitoring and diagnosis of rotating machines such as gearboxes. Although significant research has been undertaken in understanding the potential of AE in monitoring gearboxes this has been solely applied to spur gears and slow speed roller bearings. This research presents an experimental investigation that assesses the effectiveness of both AE and vibration technologies in identifying various types of defects on in a helical gearbox; the first known attempt. Furthermore, the application of advanced signals processing techniques such as Spectral kurtosis (SK) and wavelet analysis were studied on AE and vibration signatures. It is shown that the application of advanced signal processing methods is particularly necessary for monitoring helical gears. The application of SK and wavelet analysis was found to be effective in denoising the acquired signals. The first chapter of this thesis is an introduction to this research and briefly explains motivation and theoretical background supporting this research. The second chapter summaries the relevant literature to establish the current level of the knowledge in this field. The third chapter describes methodologies and experimental arrangement utilized for this investigation. Chapter 4 discusses helical gear diagnosis for both natural and seeded surface defect. Chapter 5 reports on an experimental investigation in which several technologies such as AE, vibration and motor current signature analysis, were applied to identify the presence of a naturally fatigued pinion shaft in an operating gearbox. Chapter 6 details an investigation which compared the applicability of AE and vibration technologies in monitoring a naturally degraded roller bearing. It has been concluded that AE is a strong diagnostic tool for early diagnosis of bearings faults. However, the application of condition monitoring for helical gear diagnosis was fraught with some degree of complexity as compared to spur gears. This implies that condition monitoring of the gears using AET can be challenging. On the contrary, the applicability of AET for bearing diagnosis was promising and it offered an absolute advantage over the conventional vibrationdiagnosis. Furthermore, the application advanced signals processing methods such as Spectral Kurtosis and wavelet was found to be promising in denoise the recorded AE signals. It was also concluded that the use of different signal processing methods is often necessary to achieve meaningful diagnostic information from the signals.
Author: Babak Eftekharnejad Publisher: ISBN: Category : Languages : en Pages :
Book Description
Acoustic emission (AE) is one of many technologies for health monitoring and diagnosis of rotating machines such as gearboxes. Although significant research has been undertaken in understanding the potential of AE in monitoring gearboxes this has been solely applied to spur gears and slow speed roller bearings. This research presents an experimental investigation that assesses the effectiveness of both AE and vibration technologies in identifying various types of defects on in a helical gearbox; the first known attempt. Furthermore, the application of advanced signals processing techniques such as Spectral kurtosis (SK) and wavelet analysis were studied on AE and vibration signatures. It is shown that the application of advanced signal processing methods is particularly necessary for monitoring helical gears. The application of SK and wavelet analysis was found to be effective in denoising the acquired signals. The first chapter of this thesis is an introduction to this research and briefly explains motivation and theoretical background supporting this research. The second chapter summaries the relevant literature to establish the current level of the knowledge in this field. The third chapter describes methodologies and experimental arrangement utilized for this investigation. Chapter 4 discusses helical gear diagnosis for both natural and seeded surface defect. Chapter 5 reports on an experimental investigation in which several technologies such as AE, vibration and motor current signature analysis, were applied to identify the presence of a naturally fatigued pinion shaft in an operating gearbox. Chapter 6 details an investigation which compared the applicability of AE and vibration technologies in monitoring a naturally degraded roller bearing. It has been concluded that AE is a strong diagnostic tool for early diagnosis of bearings faults. However, the application of condition monitoring for helical gear diagnosis was fraught with some degree of complexity as compared to spur gears. This implies that condition monitoring of the gears using AET can be challenging. On the contrary, the applicability of AET for bearing diagnosis was promising and it offered an absolute advantage over the conventional vibrationdiagnosis. Furthermore, the application advanced signals processing methods such as Spectral Kurtosis and wavelet was found to be promising in denoise the recorded AE signals. It was also concluded that the use of different signal processing methods is often necessary to achieve meaningful diagnostic information from the signals.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
The aim of this research project is to explore a new technique, Acoustic Emission (AE), on both the diagnostic and prognostic capabilities in monitoring gear teeth degradation (pitting), and compare with the more widely used techniques such as vibration monitoring and Spectrometric Oil Analysis (SOA). Furthermore, by employing the experimental results and past literature, a model in predicting the amount of gear surface pitting wear using AE activity level was proposed. The successful forinulation of this proposed model may be able to predict the remaining life of the gear after pitting has been detected, thereby allowing timely replacement to be carried out without the risk of catastrophic failure. A series of experimental tests which include seeded defect simulations, study on the effect of operating parameters over AE (under isothermal conditions), AE source determination tests and accelerated gear fatigue tests have been performed to investigate the diagnostics and prognostics capabilities of AE via a back-to-back gearbox set up. The experimental results achieved have highlighted some significant findings: (a) The variation in rotating speeds, change the AE levels in a much significant amount as compared to the same variation in applied load. (b) The prime source of AE was postulated to be asperity contact under rolling and sliding of the meshing gear teeth surfaces. (c) AE technique has a far better degradation (pitting) monitoring capability compared to vibration and SOA techniques. These findings have made a vast contribution in condition monitoring of gearbox using AE technique and the proposed model has also offered opportunity to make AE a potentially viable and effective tool in diagnosis and prognosis of gearbox or even other rotating machinery defects.
Author: Trevor J. Holroyd Publisher: ISBN: 9781901892079 Category : Acoustic emission Languages : en Pages : 147
Book Description
This book is intended for those using, wishing to use, or wishing to understand, the use of Acoustic Emissions/Ultrasonics applied to the condition monitoring of industrial machinery. This technique provides a relatively easy way to improve monitoring of traditionally awkward items such as plain bearings, slow speed machinery, worm/wheel gears and machines of either unknown design or for which there have been no previous measurements. This is a practical book, touching on the basic theory and concepts, available equipment and practical issues relevant to the engineer as well as highlighting several case studies with which the reader can relate. The book is laid out in such a way that its chapters and sections follow a logical order for those wishing to read from cover to cover, but more importantly it can be used for quick reference purposes on a topic by topic basis.
Author: Markus G. R. Sause Publisher: Springer Nature ISBN: 3030721922 Category : Aerospace engineering Languages : en Pages : 292
Book Description
This open access book presents established methods of structural health monitoring (SHM) and discusses their technological merit in the current aerospace environment. While the aerospace industry aims for weight reduction to improve fuel efficiency, reduce environmental impact, and to decrease maintenance time and operating costs, aircraft structures are often designed and built heavier than required in order to accommodate unpredictable failure. A way to overcome this approach is the use of SHM systems to detect the presence of defects. This book covers all major contemporary aerospace-relevant SHM methods, from the basics of each method to the various defect types that SHM is required to detect to discussion of signal processing developments alongside considerations of aerospace safety requirements. It will be of interest to professionals in industry and academic researchers alike, as well as engineering students. This article/publication is based upon work from COST Action CA18203 (ODIN - http://odin-cost.com/), supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation.
Author: Hosameldin Ahmed Publisher: John Wiley & Sons ISBN: 1119544629 Category : Technology & Engineering Languages : en Pages : 456
Book Description
Provides an extensive, up-to-date treatment of techniques used for machine condition monitoring Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoringguiding readers from the basics of rotating machines to the generation of knowledge using vibration signals Provides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costs Features learning algorithms that can be used for fault diagnosis and prognosis Includes previously and recently developed dimensionality reduction techniques and classification algorithms Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.
Author: Babak Eftekharnejad
Author: Babak Eftekharnejad
Author: Mhmod A. A. Hamel
Author: R. I Raja Hamzah
Author: K. H. Pedersen
Author: Cristián Molina Vicuña
Author: 
Author: Hua Shyang Ling
Author: Trevor J. Holroyd
Author: Markus G. R. Sause
Author: Hosameldin Ahmed