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Author: Ahmed Felkaoui
Publisher: Springer
ISBN: 3319961810
Category : Technology & Engineering
Languages : en
Pages : 142
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Book Description
This book provides readers with a timely snapshot of the potential offered by and challenges posed by signal processing methods in the field of machine diagnostics and condition monitoring. It gathers contributions to the first Workshop on Signal Processing Applied to Rotating Machinery Diagnostics, held in Setif, Algeria, on April 9-10, 2017, and organized by the Applied Precision Mechanics Laboratory (LMPA) at the Institute of Precision Mechanics, University of Setif, Algeria and the Laboratory of Mechanics, Modeling and Manufacturing (LA2MP) at the National School of Engineers of Sfax. The respective chapters highlight research conducted by the two laboratories on the following main topics: noise and vibration in machines; condition monitoring in non-stationary operations; vibro-acoustic diagnosis of machinery; signal processing and pattern recognition methods; monitoring and diagnostic systems; and dynamic modeling and fault detection.
Author: Ahmed Felkaoui
Publisher: Springer
ISBN: 3319961810
Category : Technology & Engineering
Languages : en
Pages : 142
Get Book Here
Book Description
This book provides readers with a timely snapshot of the potential offered by and challenges posed by signal processing methods in the field of machine diagnostics and condition monitoring. It gathers contributions to the first Workshop on Signal Processing Applied to Rotating Machinery Diagnostics, held in Setif, Algeria, on April 9-10, 2017, and organized by the Applied Precision Mechanics Laboratory (LMPA) at the Institute of Precision Mechanics, University of Setif, Algeria and the Laboratory of Mechanics, Modeling and Manufacturing (LA2MP) at the National School of Engineers of Sfax. The respective chapters highlight research conducted by the two laboratories on the following main topics: noise and vibration in machines; condition monitoring in non-stationary operations; vibro-acoustic diagnosis of machinery; signal processing and pattern recognition methods; monitoring and diagnostic systems; and dynamic modeling and fault detection.
Author: Hosameldin Ahmed
Publisher: John Wiley & Sons
ISBN: 1119544629
Category : Technology & Engineering
Languages : en
Pages : 456
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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: Peeters Cédric
Publisher:
ISBN:
Category :
Languages : en
Pages : 235
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Book Description
This Ph.D. dissertation targets innovative methods for vibration-based condition monitoring of rotating machinery. Substantial benefits can be achieved from an economical and a safety point of view using condition monitoring. One of the most popular methods to gather information about the state of machine parts is through the analysis of machine vibrations. Most of these vibrations are directly linked to periodical behavior of subsystems within the machine like e.g. rotating shafts, gears, rotating electrical fields, etc. This knowledge can be exploited to enable faultdependent processing schemes. This dissertation investigates how to implement and utilize these processing schemes and details the steps in such a procedure. Typically, the first prerequisite for advanced analysis is the availability of the instantaneous rotation speed. This speed needs to be known since most frequency-based analysis techniques assume stationary behavior. Knowledge of the speed thus allows for compensating speed fluctuations, for example through angular resampling of the vibration signal. While there are hardware-based solutions for speed estimation using angle encoders or tachometers, this thesis investigates the potential in vibration signals for speed estimation. After speed estimation and angular resampling, a common next step is to separate the signal into deterministic and stochastic components. The cepstrum editing procedure is examined for its efficacy and applicability. Afterwards, different filtering methods are inspected as to improve the signal-to-noise ratio of the signal content of interest. Existing methods using conventional criteria are investigated together with a novel blind filtering methodology. The final step in the multi-step processing scheme is to search for the potential fault. Statistical indicators can be calculated on the processed time domain signal and tracked over time to check for increases. In many cases, the fault signature exhibits cyclostationary behavior. Therefore this dissertation also examines different cyclostationary analysis techniques. Lastly, the performance of the different processing methods is validated on two experimental vibration data sets of wind turbine gearboxes.
Author: Yaguo Lei
Publisher: Butterworth-Heinemann
ISBN: 0128115351
Category : Technology & Engineering
Languages : en
Pages : 376
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Book Description
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. The main contents include multi-domain signal processing and feature extraction, intelligent diagnosis models, clustering algorithms, hybrid intelligent diagnosis strategies, and RUL prediction approaches, etc. This book presents fundamental theories and advanced methods of identifying the occurrence, locations, and degrees of faults, and also includes information on how to predict the RUL of rotating machinery. Besides experimental demonstrations, many application cases are presented and illustrated to test the methods mentioned in the book. This valuable reference provides an essential guide on machinery fault diagnosis that helps readers understand basic concepts and fundamental theories. Academic researchers with mechanical engineering or computer science backgrounds, and engineers or practitioners who are in charge of machine safety, operation, and maintenance will find this book very useful. Provides a detailed background and roadmap of intelligent diagnosis and RUL prediction of rotating machinery, involving fault mechanisms, vibration characteristics, health indicators, and diagnosis and prognostics Presents basic theories, advanced methods, and the latest contributions in the field of intelligent fault diagnosis and RUL prediction Includes numerous application cases, and the methods, algorithms, and models introduced in the book are demonstrated by industrial experiences
Author: Fakher Chaari
Publisher: Springer Nature
ISBN: 3030795195
Category : Technology & Engineering
Languages : en
Pages : 177
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Book Description
This book offers an overview of current methods for the intelligent monitoring of rotating machines. It describes the foundations of smart monitoring, guiding readers to develop appropriate machine learning and statistical models for answering important challenges, such as the management and analysis of a large volume of data. It also discusses real-world case studies, highlighting some practical issues and proposing solutions to them. The book offers extensive information on research trends, and innovative strategies to solve emerging, practical issues. It addresses both academics and professionals dealing with condition monitoring, and mechanical and production engineering issues, in the era of industry 4.0.
Author: Maurice L. Adams
Publisher: CRC Press
ISBN: 9780203902165
Category : Technology & Engineering
Languages : en
Pages : 642
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Book Description
This comprehensivereference/text provides a thorough grounding in the fundamentals of rotating machinery vibration-treating computer model building, sources and types of vibration, and machine vibration signal analysis. Illustrating turbomachinery, vibration severity levels, condition monitoring, and rotor vibration cause identification, Ro
Author: Osami Matsushita
Publisher: Springer
ISBN: 9784431554523
Category : Technology & Engineering
Languages : en
Pages : 573
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Book Description
Building on the previous volume “Vibrations of Rotating Machinery - Volume 1. Basic Rotordynamics: Introduction to Practical Vibration Analysis,” this book is intended for all practical designers and maintenance experts who are responsible for the reliable manufacturing and operation of rotating machinery. It opens with the dynamics of oil film bearings and their influences on unbalance, vibration resonance and the stability of rotor whirl motion. Subsequently, the book introduces readers to vibration diagnosis techniques for traditional ball bearings and active vibration control from magnetic bearings. Case studies on vibration problems and troubleshooting in industrial turbo machines are then presented and explained, showing rotor designers how to eliminate instability and modify resonance characteristics. Torsional vibration and other coupled vibration phenomena are discussed, and vibration measurement techniques and related signal processing procedures for vibration diagnosis are provided. Our latest three topics are included, covering: (a) the importance of the modeling order reduction (MOR) technique; (b) the approximate evaluation for oil-wheel/whip instability; and (c) a systematic method for shafting-blading coupled vibration analyses. In closing, a 100-question trial test is supplied as an example of the certification of vibration experts based on the ISO standard.
Author: Shilpa Reddy Pantula
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Rotating machinery are an important part of industrial equipment. Their components are subjected to harsh operating environments, and hence experience significant wear and tear. It is necessary that they function efficiently all the time in order to avoid significant monetary losses and down-time. Monitoring the health of such machinery components has become an essential part in many industries to ensure their continuous operation and avoiding loss in productivity. Traditionally, signal processing methods have been employed to analyze the vibration signals emitted from rotating machines. With time, the complexity of machinery components has increased, which makes the process of condition monitoring complex and time consuming, and consequently costly. Hence, a paradigm shift in condition monitoring methods towards data-driven approaches has recently taken place towards reducing complexity in estimation, where the monitoring of machinery is focused on purely data-driven methods. In this thesis, a novel data-driven framework to condition monitoring of gearbox is studied and illustrated using simulated and experimental vibration signals. This involves analyzing the signal, deriving feature sets and using machine learning algorithms to discern the condition of machinery. The algorithm is implemented on data from a drivetrain dynamics simulator (DDS), equipment designed by Spectraquest Inc. for academic and industrial research purposes. Datasets from pristine state and faulty gearboxes are collected and the algorithms are tested against this data. This framework has been developed to facilitate automated monitoring of machinery in industries, thus reducing the need for manual supervision and interpretation.
Author: Wei Guo
Publisher:
ISBN:
Category : Science
Languages : en
Pages :
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Book Description
Vibration-based condition monitoring and fault diagnosis are becoming more common in the industry to increase machine availability and reliability. Considerable research efforts have recently been directed towards the development of adaptive signal processing methods for fault diagnosis. Two adaptive signal decomposition methods, id est the empirical mode decomposition (EMD) and the local mean decomposition (LMD), are widely used. This chapter is intended to summarize the recent developments mostly based on the authors' works. It aims to provide a valuable reference for readers on the processing and analysis of vibration signals collected from rotating machinery.
Author: Gustavo Ibarguen
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Rotating machinery is used in a variety of essential engineering systems, including motors, pumps, compressors, and gearboxes. The gas, oil, power, manufacturing, and process industries rely heavily on rotating machines. Their failures can be very expensive and lead to a decrease in production so proper maintenance is essential. Condition based maintenance is a relatively new strategy of performing maintenance on equipment when signal processing of sensor signals indicates a failure may be imminent. The most popular sensors for condition based maintenance measure the vibration of the rotating machine. These sensors provide information about the overall state of the machine and point to potential faults. This thesis studies the effectiveness of analyzing vibration data to determine the state of operation of rotating machine systems. Specifically, research and experiments are performed to discover if vibration signatures can determine if a system has certain faults, such as shaft misalignment, unbalance, or deformation in shaft couplings. The presence or absence of these faults can lead to the determination of the health of operation of a rotating machine system.
