Contributions to the Analysis of Vibrations and Acoustic Emissions for the Condition Monitoring of Epicyclic Gearboxes PDF Download

Author: Cristián Molina Vicuña
Publisher:
ISBN: 9783941277069
Category :
Languages : en
Pages : 138

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Author: Mohamed Haddar
Publisher: Springer
ISBN: 3319666975
Category : Technology & Engineering
Languages : en
Pages : 1225

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This book offers a collection of original peer-reviewed contributions presented at the 7th International Congress on Design and Modeling of Mechanical Systems (CMSM’2017), held in Hammamet, Tunisia, from the 27th to the 29th of March 2017. It reports on both research findings, innovative industrial applications and case studies concerning mechanical systems and related to modeling and analysis of materials and structures, multiphysics methods, nonlinear dynamics, fluid structure interaction and vibroacoustics, design and manufacturing engineering. Continuing on the tradition of the previous editions, this proceedings offers a broad overview on the state-of-the art in the field and a useful resource for academic and industry specialists active in the field of design and modeling of mechanical systems. CMSM’2017 was jointly organized by two leading Tunisian research laboratories: the Mechanical, Modeling and Manufacturing Laboratory of the National Engineering School of Sfax and the Mechanical Engineering Laboratory of the National Engineering School of Monastir..

Author: Adam Jablonski
Publisher: Springer Nature
ISBN: 3030627497
Category : Technology & Engineering
Languages : en
Pages : 542

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This book offers the first comprehensive and practice-oriented guide to condition monitoring algorithms in MATLAB®. After a concise introduction to vibration theory and signal processing techniques, the attention is moved to the algorithms. Each signal processing algorithm is presented in depth, from the theory to the application, and including extensive explanations on how to use the corresponding toolbox in MATLAB®. In turn, the book introduces various techniques for synthetic signals generation, as well as vibration-based analysis techniques for large data sets. A practical guide on how to directly access data from industrial condition monitoring systems (CMS) using MATLAB® .NET Libraries is also included. Bridging between research and practice, this book offers an extensive guide on condition monitoring algorithms to both scholars and professionals. “Condition Monitoring Algorithms in MATLAB® is a great resource for anyone in the field of condition monitoring. It is a unique as it presents the theory, and a number of examples in Matlab®, which greatly improve the learning experience. It offers numerous examples of coding styles in Matlab, thus supporting graduate students and professionals writing their own codes." Dr. Eric Bechhoefer Founder and CEO of GPMS Developer of the Foresight MX Health and Usage Monitoring System

Author: Anna Timofiejczuk
Publisher: Springer
ISBN: 3319620428
Category : Technology & Engineering
Languages : en
Pages : 514

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This book provides readers with an overview of recent theories and methods for machinery diagnostics applied to machinery maintenance. Each chapter, accepted after a rigorous peer-review process, reports on a selected, original piece of work discussed at the International Congress on Technical Diagnostics, ICTD2016, held on September 12 – 16, 2016, in Gliwice, Poland. The book covers a broad range of topics, including machines operating in non-stationary conditions, and examples from different industrial fields of mechanical, civil, computer and electronic engineering as well as the medical, food, automotive, and mining industries. By presenting state-of-the-art diagnostic solutions and discussing important industrial issues the book offers a valuable resource to both academics and professionals as well as a bridge to facilitate communication and collaboration between the two groups.

Author: Babak Eftekharnejad
Publisher:
ISBN:
Category :
Languages : en
Pages :

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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 :

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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: Czesław Cempel
Publisher: Prentice Hall
ISBN:
Category : Science
Languages : en
Pages : 236

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Book Description
Vibration condition is a relatively new area of technology. This text opens with an outline of its origins and presents possible applications of the technique, showing the role of vibration diagnostics and vibration condition monitoring (VCM) during the machine running stage.

Author: Peter Paul Schon
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
Condition monitoring of epicyclic gearboxes through vibration signature analysis, with particular focus on time domain methods and the use of adaptive filtering techniques for the purpose of signal enhancement, is the central theme of this work. Time domain filtering methods for the purpose of removal of random noise components from periodic, but not necessarily stationary or cyclostationary, signals are developed. Damage identification is accomplished through vibration signature analysis by nonstationary timefrequency methods, belonging to Cohen's general class of time-frequency distributions, strictly based in the time domain. Although a powerful and commonly used noise reduction technique, synchronous averaging requires alternate sensors in addition to the vibration pickup. For this reason the use of time domain techniques that employ only the vibration data is investigated. Adaptive filters may be used to remove random noise from the nonstationary signals considered. The well-known Least Mean Squares algorithm is employed in an adaptive line enhancer configuration. To counter the much discussed convergence difficulties that are often experienced when the least mean squares algorithm is applied, a new unconditionally convergent algorithm based on the spherical quadratic steepest descent method is presented. The spherical quadratic steepest descent method has been shown to be unconditionally convergent when applied to a quadratic objective function. Time-frequency methods are succinctly employed to analyse the vibration signals simultaneously in the time and frequency domains. Transients covering a wide frequency range are a clear and definite indication of impacting events as gear teeth mate, and observation of such events on a timefrequency distribution are used to indicate damage to the transmission. The pseudo Wigner-Ville distribution and the Spectrogram, both belonging to Cohen's general class of time-frequency distributions are comparatively used to the end of damage identification. It is shown that an unconditionally convergent adaptive filtering technique used in conjunction with time-frequency methods can indicate a damaged condition in an epicyclic gearbox, where the non-adaptively filtered data did not present clear indications of damage.

Author: S. B. Glavatskikh
Publisher:
ISBN:
Category :
Languages : en
Pages : 170

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Author: Salem Al-Arbi
Publisher:
ISBN:
Category :
Languages : en
Pages :

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It is often impractical to measure vibrations directly at /or close to their sources when condition monitoring gearbox systems. It is common to measure the vibration distant from the source due to limited access to the component which is to be monitored. In addition, operating the gearbox under different loads and speeds also produces vibration signals within different components. Vibration measured in this way may be significantly distorted by the effect of signal transmission paths and interference from other sources. Therefore, suppression of distortions is a key issue for remote measurement based condition monitoring. In this research work, the influences of transducer locations and operating conditions on the vibration signal have been investigated on a typical gearbox transmission system for the detection of faults induced within the gearbox. Vibration signals corresponding to a healthy (baseline) and faulty conditions on two-stage helical gearbox at various load and speed levels were recorded. The baseline vibration data were examined using conventional methods in the time, frequency and the joint time-frequency domains, and are referenced for comparison with more advanced methods. Several parameters have been proposed for monitoring gear condition locally (gearbox casing) including time, frequency, and joint time-frequency domain representation. The results show that traditional signal processing techniques were insufficient for revealing fault detection information due to the low signal to noise ratio (SNR). This research also presents a mathematical model for the simulation of vibration signals in order to further understand the source of the vibration. The model represents a two stage gear system using a suitable stiffness function to represent the forces acting between each pair of gears. Rotational stiffness and damping are also used to simulate the angular motion of the gears and shafts. Results show that the frequency spectrum of acceleration outputs from the model take the expected form with peaks at the meshing frequency and associated harmonics. Furthermore, if the stiffness function between the first pair of gears is simulated with a broken tooth, and various degrees of damage, outputs from the simulation have similar sideband effects to the signals produced in the experimental investigation. In addition, the model also demonstrates that variation of load and speed produces a corresponding effect to that seen in the experiments. Consequently, although relatively simple, the mathematical model can be used to explain vibration mechanisms in real gearbox systems used in condition monitoring. Time synchronous averaging (TSA) has been applied to the vibration signals from the gearbox to remove random noise combined with the raw signal. The angular domain signal, the order spectrum and the order-frequency presentation were used to characterise gearbox vibration in these new domains in more detail. Results obtained following TSA were compared with those obtained through conventional analysis from waveform characteristics, spectrum patterns and corresponding feature parameters under different operating loads and fault conditions. In addition, continuous wavelet transform (CWT) of TSA was also compared with the conventional CWT results of raw signals to further characterise vibrations. As part of this research study, the vibration transmission path has been estimated using the frequency response function (FRF) technique. A response based estimation method has been developed to revise the base path and adapted to operating conditions for more accurate fault estimation. Both theoretical analysis and test results showed that improved diagnosis when the path information was included in vibration signal processing and feature selection. Finally, the vibration data recorded from the two accelerometers located on the gearbox casing and motor flange were analyzed using different signal processing methods to investigate the effect of path transmission (transducer location) on the detection and diagnosis of the seeded gear tooth faults. Results from the angular domain, the order spectrum and the order-frequency analysis are presented to demonstrate use of these techniques for fault detection in gearboxes and that the effect of path transmissions can be observed on the vibration signals. Results showed that CWT of the TSA signal could be used to detect and indicate the severity of the gear damage effectively even if vibration signals originated from a remote motor flange.