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.

"Without doubt the best modern and up-to-date text on the topic, wirtten by one of the world leading experts in the field. Should be on the desk of any practitioner or researcher involved in the field of Machine Condition Monitoring" Simon Braun, Israel Institute of Technology Explaining complex ideas in an easy to understand way, Vibration-based Condition Monitoring provides a comprehensive survey of the application of vibration analysis to the condition monitoring of machines. Reflecting the natural progression of these systems by presenting the fundamental material and then moving onto detection, diagnosis and prognosis, Randall presents classic and state-of-the-art research results that cover vibration signals from rotating and reciprocating machines; basic signal processing techniques; fault detection; diagnostic techniques, and prognostics. Developed out of notes for a course in machine condition monitoring given by Robert Bond Randall over ten years at the University of New South Wales, Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications is essential reading for graduate and postgraduate students/ researchers in machine condition monitoring and diagnostics as well as condition monitoring practitioners and machine manufacturers who want to include a machine monitoring service with their product. Includes a number of exercises for each chapter, many based on Matlab, to illustrate basic points as well as to facilitate the use of the book as a textbook for courses in the topic. Accompanied by a website www.wiley.com/go/randall housing exercises along with data sets and implementation code in Matlab for some of the methods as well as other pedagogical aids. Authored by an internationally recognised authority in the area of condition monitoring.

Mechanical Vibrations and Condition Monitoring presents a collection of data and insights on the study of mechanical vibrations for the predictive maintenance of machinery. Seven chapters cover the foundations of mechanical vibrations, spectrum analysis, instruments, causes and effects of vibration, alignment and balancing methods, practical cases, and guidelines for the implementation of a predictive maintenance program. Readers will be able to use the book to make predictive maintenance decisions based on vibration analysis. This title will be useful to senior engineers and technicians looking for practical solutions to predictive maintenance problems. However, the book will also be useful to technicians looking to ground maintenance observations and decisions in the vibratory behavior of machine components. Presents data and insights into mechanical vibrations in condition monitoring and the predictive maintenance of industrial machinery Defines the key concepts related to mechanical vibration and its application for predicting mechanical failure Describes the dynamic behavior of most important mechanical components found in industrial machinery Explains fundamental concepts such as signal analysis and the Fourier transform necessary to understand mechanical vibration Provides analysis of most sources of failure in mechanical systems, affording an introduction to more complex signal analysis

This book describes in detail different types of vibration signals and the signal processing methods, including signal resampling and signal envelope, used for condition monitoring of drivetrains. A special emphasis is placed on wind turbines and on the fact that they work in highly varying operational conditions. The core of the book is devoted to cutting-edge methods used to validate and process vibration data in these conditions. Key case studies, where advanced signal processing methods are used to detect failures of gearboxes and bearings of wind turbines, are described and discussed in detail. Vibration sensors, SCADA (Supervisory Control and Data Acquisition), portable data analyzers and online condition monitoring systems, are also covered. This book offers a timely guide to both researchers and professionals working with wind turbines (but also other machines), and to graduate students willing to extend their knowledge in the field of vibration analysis.

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

Vibration-based Condition Monitoring Stay up to date on the newest developments in machine condition monitoring with this brand-new resource from an industry leader The newly revised Second Edition of Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications delivers a thorough update to the most complete discussion of the field of machine condition monitoring. The distinguished author offers readers new sections on diagnostics of variable speed machines, including wind turbines, as well as new material on the application of cepstrum analysis to the separation of forcing functions, structural model properties, and the simulation of machines and faults. The book provides improved methods of order tracking based on phase demodulation of reference signals and new methods of determining instantaneous machine speed from the vibration response signal. Readers will also benefit from an insightful discussion of new methods of calculating the Teager Kaiser Energy Operator (TKEO) using Hilbert transform methods in the frequency domain. With a renewed emphasis on the newly realized possibility of making virtual instruments, readers of Vibration-based Condition Monitoring will benefit from the wide variety of new and updated topics, like: A comprehensive introduction to machine condition monitoring, including maintenance strategies, condition monitoring methods, and an explanation of the basic problem of condition monitoring An exploration of vibration signals from rotating and reciprocating machines, including signal classification and torsional vibrations An examination of basic and newly developed signal processing techniques, including statistical measures, Fourier analysis, Hilbert transform and demodulation, and digital filtering, pointing out the considerable advantages of non-causal processing, since causal processing gives no benefit for condition monitoring A discussion of fault detection, diagnosis and prognosis in rotating and reciprocating machines, in particular new methods using fault simulation, since “big data” cannot provide sufficient data for late-stage fault development Perfect for machine manufacturers who want to include a machine monitoring service with their product, Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications will also earn a place in university and research institute libraries where there is an interest in machine condition monitoring and diagnostics.

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.

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.

Provides Typical Abstract Representations of Different Steps for Analyzing Any Dynamic SystemVibration and dynamics are common in everyday life, and the use of vibration measurements, tests, and analyses is becoming standard for various applications. Vibration Analysis, Instruments, and Signal Processing focuses on the basic understanding of vibrat

Vibration-based condition monitoring (VCM) is a well-accepted approach in industries for early detection of any defect, thereby triggering the maintenance process and ultimately reducing overheads and plant downtime. A number of vibration instruments, data analyzer and related hardware and software codes are developed to meet the industry requirements. This book aims to address issues faced by VCM professionals, such as frequency range estimation for vibration measurements, sensors, data collection and data analyzer including related parameters which are explained through step-by-step approaches. Each chapter is written in the tutorial style with experimental and/or industrial examples for clear understanding.