A Fault Diagnosis System for Rotary Machinery Supported by Rolling Element Bearings PDF Download

Author: Shahab Hasanzadeh Ghafari
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Michael Louis Adams
Publisher:
ISBN:
Category : Church and state
Languages : en
Pages : 372

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Author: IMechE (Institution of Mechanical Engineers)
Publisher: John Wiley & Sons
ISBN: 9781860584473
Category : Technology & Engineering
Languages : en
Pages : 750

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Book Description
This essential text contains the papers from the 8th international IMechE conference on Vibrations in Rotating Machinery held at the University of Wales, Swansea in September 2004. The themes of the volume are new developments and industrial applications of current technology relevant to the vibration and noise of rotating machines and assemblies. TOPICS INCLUDE Rotor balancing – including active and automatic balancing Special rotating machines – including micromachines Oil film bearings and dampers Active control methods for rotating machines Smart machine technology Dynamics of assembled rotors Component life predictions and life extension strategies The dynamics of geared systems Cracked rotors – detection, location ad prognosis Chaotic behaviour in machines Experimental methods and discoveries.

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: Rajiv Tiwari
Publisher: CRC Press
ISBN: 1351863630
Category : Science
Languages : en
Pages : 1225

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Book Description
The purpose of this book is to give a basic understanding of rotor dynamics phenomena with the help of simple rotor models and subsequently, the modern analysis methods for real life rotor systems. This background will be helpful in the identification of rotor-bearing system parameters and its use in futuristic model-based condition monitoring and, fault diagnostics and prognostics. The book starts with introductory material for finite element methods and moves to linear and non-linear vibrations, continuous systems, vibration measurement techniques, signal processing and error analysis, general identification techniques in engineering systems, and MATLAB analysis of simple rotors. Key Features: • Covers both transfer matrix methods (TMM) and finite element methods (FEM) • Discusses transverse and torsional vibrations • Includes worked examples with simplicity of mathematical background and a modern numerical method approach • Explores the concepts of instability analysis and dynamic balancing • Provides a basic understanding of rotor dynamics phenomena with the help of simple rotor models including modern analysis methods for real life rotor systems.

Author: Alaa Abdulhady Jaber
Publisher: Springer
ISBN: 331944932X
Category : Technology & Engineering
Languages : en
Pages : 302

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This thesis introduces a successfully designed and commissioned intelligent health monitoring system, specifically for use on any industrial robot, which is able to predict the onset of faults in the joints of the geared transmissions. However the developed embedded wireless condition monitoring system leads itself very well for applications on any power transmission equipment in which the loads and speeds are not constant, and access is restricted. As such this provides significant scope for future development. Three significant achievements are presented in this thesis. First, the development of a condition monitoring algorithm based on vibration analysis of an industrial robot for fault detection and diagnosis. The combined use of a statistical control chart with time-domain signal analysis for detecting a fault via an arm-mounted wireless processor system represents the first stage of fault detection. Second, the design and development of a sophisticated embedded microprocessor base station for online implementation of the intelligent condition monitoring algorithm, and third, the implementation of a discrete wavelet transform, using an artificial neural network, with statistical feature extraction for robot fault diagnosis in which the vibration signals are first decomposed into eight levels of wavelet coefficients.

Author: Huan Huang
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Bearings are among the most commonly used components in rotating machines and bearing fault diagnosis has been widely investigated, especially with vibration signal based techniques. Generally, bearing faults can be diagnosed in the frequency domain since each type of fault has a specific Fault Characteristic Frequency (FCF) proportional to the rotational frequency. However, this approach is complicated by the facts that: (1) bearing vibration signals are often contaminated by random noise and interference signals transmitted from other sources, (2) bearings often operate under time-varying speed conditions which make the FCF also time-varying. To address the problems for bearing fault signature extraction, new methods are proposed in this thesis. For the constant speed case, bearing fault signature extraction using the method of Oscillatory Behavior-based Signal Decomposition (OBSD) is investigated, which includes: (1) effects of parameter selection and (2) automatic parameter selection of OBSD for bearing fault signature extraction. For bearing fault diagnosis under time-varying speed conditions, research based on the time-frequency technique is conducted, including (1) a multiple time-frequency curve extraction algorithm and (2) a resampling-free and tachometer-free method for the time-varying speed case with the presence of interferences, and (3) proposing a short-time kurtogram for bearing fault signature extraction under time-varying speed conditions. The effectiveness of the proposed methods in this thesis has been validated by simulated signals and experimental data.

Author: Christopher Niezrecki
Publisher: Springer
ISBN: 3030129357
Category : Technology & Engineering
Languages : en
Pages : 122

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Book Description
​Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics, 2019, the sixth volume of eight from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Health Monitoring, including papers on: Novel Techniques Optical Methods, Scanning LDV Methods Photogrammetry & DIC Rotating Machinery

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: Rupinder Singh
Publisher: CRC Press
ISBN: 1000549011
Category : Technology & Engineering
Languages : en
Pages : 220

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Book Description
This cross-disciplinary book transcends departmental, institutional, industrial, public, and research organizations and goes beyond global barriers to cover the integration of research, education, and manufacturing in advanced materials processing and characterization, including CAD-CAM, Finite Element Analysis (FEA), and smart manufacturing. Advances in Manufacturing Technology: Computational Materials Processing and Characterization focuses on the design of experiment-based computational models, which involves FEA along with an ergonomics-based design of tooling for both conventional and nonconventional manufacturing processes. It discusses research, work, and recent developments in the field of production manufacturing of any mechanical system. Case studies and solved numerical solutions are included at the end of each chapter for easy reading comprehension. The book is helpful to those working on new developments in the field of product manufacturing. It also acts as a first-hand source of information for academic scholars and commercial manufacturers as they make strategic manufacturing development plans.