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This book provides the tools to enhance the precision, automation and intelligence of modern CNC machining systems. Based on a detailed description of the technical foundations of the machining monitoring system, it develops the general idea of design and implementation of smart machining monitoring systems, focusing on the tool condition monitoring system. The book is structured in two parts. Part I discusses the fundamentals of machining systems, including modeling of machining processes, mathematical basics of condition monitoring and the framework of TCM from a machine learning perspective. Part II is then focused on the applications of these theories. It explains sensory signal processing and feature extraction, as well as the cyber-physical system of the smart machining system. Its utilisation of numerous illustrations and diagrams explain the ideas presented in a clear way, making this book a valuable reference for researchers, graduate students and engineers alike.

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.

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 monitoring�guiding 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.

This book discusses condition based monitoring of rotating machines using intelligent adaptive systems. The book employs computational intelligence and fuzzy control principles to deliver a module that can adaptively monitor and optimize machine health and performance. This book covers design and performance of such systems and provides case studies and data models for fault detection and diagnosis. The contents cover everything from optimal sensor positioning to fault diagnosis. The principles laid out in this book can be applied across rotating machinery such as turbines, compressors, and aircraft engines. The adaptive fault diagnostics systems presented can be used in multiple time and safety critical applications in domains such as aerospace, automotive, deep earth and deep water exploration, and energy.

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

Forsthoffer's Component Conditioning Monitoring Handbook is dedicated to the condition monitoring of all rotating equipment. It describes the Forsthoffer Associates method of Component Condition Monitoring (CCM) and gives the reader detailed instructions on what to monitor for each component type. The author's easy and approachable style makes this a very practical reference for any level of technical background. This represents the latest addition to a set that includes volumes on 1. Fundamentals of Rotating Equipment; 2. Pumps; 3. Compressors; 4. Auxiliary Systems; 5. Reliability Optimization through Component Condition Monitoring and Root Cause Analysis'. - Contains separate chapters that address each of the 5 major components of all rotating machinery - Covers what parameters must be monitored when action is required to prevent unplanned shutdown - Describes how to use specific spreadsheets for condition monitoring tasks that can be downloaded from a companion website

This book offers readers a concise yet comprehensive introduction to a set of diagnostic methods for on-line condition monitoring of lubricated tribosystems used in industry. It covers the latest trends in on-line tribodiagnostics, an important and rapidly developing area of tribology. The book also reports on new tools as they have been developed and applied by the authors. A special emphasis is given to the physical fundamentals of opto-magnetic detectors, ferro-analyzers and analyzers of metal particles in lubricated tribosystems, as well as fluorescence methods for real-time oil monitoring in compressors, hydraulic systems and electrical transformers. Further, the book discusses other important issues such as the monitoring of water content in oil, and presents techniques for measuring soot content in oil in diesel engine oils. Lastly, it describes the modular intelligent (SMART) diagnostic system for vehicles. Mainly intended for researchers, industrial and automotive engineers developing cost-effective techniques and sensors for the on-line monitoring of lubricating oil, the book also offers a valuable source of information for students and project managers in the manufacturing, energy, oil and gas, and automotive industry.

In the fast pace of the modern world it is important, more than ever, for factories to know how and why their machines are failing and what can be done to prevent it. As such, it is imperative that new research is conducted to make sure that factories can operate as efficiently as possible. Fuzzy Logic Dynamics and Machine Prediction for Failure Analysis is an essential reference source for the newest research on the risk assessment matrix, ladder logic, and computerized maintenance management systems (CMMS). Featuring widespread coverage across a variety of related viewpoints and topics, such as the Ishikawa diagram, machinery failure analysis and troubleshooting, model reference adaptive control systems, and proportional–integral–derivative (PID) controllers, this book is ideally designed for professionals, upper-level students, and academics seeking current research on the implementation of fuzzy logic in machine prediction failure.

This book addresses issues and challenges of performance measurement in the maintenance function. It presents a proposal of indicators with a framework that classifies and sorts regarding functional and hierarchical aspects. The book has been developed with different aspects of traditional literature, i.e. several frameworks (or natural groupings) like BSC Balanced Scorecard, Multicriteria framework, Neely Prism and adaptation of these frameworks. Hierarchies for the use of indicators and benchmark values are provided to allow quantification, comparison and emission of recommendations.

Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniquesConsiders the merits of e

Consider a Viable and Cost-Effective Platform for the Industries of the Future (IOF)Benefit from improved safety, performance, and product deliveries to your customers. Achieve a higher rate of equipment availability, performance, product quality, and reliability. Integrated Reliability: Condition Monitoring and Maintenance of Equipment incorporate

Find the Fault in the Machines- Drawing on the author's more than two decades of experience with machinery condition monitoring and consulting for industries in India and abroad, Machinery Condition Monitoring: Principles and Practices introduces the practicing engineer to the techniques used to effectively detect and diagnose faults in machines. Providing the working principle behind the instruments, the important elements of machines as well as the technique to understand their conditions, this text presents every available method of machine fault detection occurring in machines in general, and rotating machines in particular.A Single-Source Solution for Practice Machinery Conditioning Monitoring- Since vibration is one of the most widely used fault detection techniques, the book offers an assessment of vibration analysis and rotor-dynamics. It also covers the techniques of wear and debris analysis, and motor current signature analysis to detect faults in rotating mechanical systems as well as thermography, the nondestructive test NDT techniques (ultrasonics and radiography), and additional methods. The author includes relevant case studies from his own experience spanning over the past 20 years, and detailing practical fault diagnosis exercises involving various industries ranging from steel and cement plants to gas turbine driven frigates. While mathematics is kept to a minimum, he also provides worked examples and MATLAB® codes.This book contains 15 chapters and provides topical information that includes: A brief overview of the maintenance techniques Fundamentals of machinery vibration and rotor dynamics Basics of signal processing and instrumentation, which are essential for monitoring the health of machines Requirements of vibration monitoring and noise monitoring Electrical machinery faults Thermography for condition monitoring Techniques of wear debris analysis and some of the nondestructive test (NDT) techniques for condition monitoring like ultrasonics and radiography Machine tool condition monitoring Engineering failure analysis Several case studies, mostly on failure analysis, from the author's consulting experience Machinery Condition Monitoring: Principles and Practices presents the latest techniques in fault diagnosis and prognosis, provides many real-life practical examples, and empowers you to diagnose the faults in machines all on your own.

This book presents the main methods and techniques for measuring and monitoring the accuracy of geometrical parameters of precision Computer Numerically Controlled (CNC) and automated machines, including modern coordinate measuring machines (CMMs). Highlights include: • Standard methods and means of testing, together with methods newly developed and tested by the authors; • Various parameters, such as straightness, perpendicularity, flatness, pitch, yaw, and roll, as well as the principal processes for measurement of these parameters; • Lists and tables of geometrical accuracy parameters, together with diagrams of arrangements for their control and evaluation of measurement results; • Special methods and some original new devices for measurement and monitoring, information measuring systems (consisting of laser interferometers, photoelectric raster encoders or scales, etc.), and methods for the measurement and testing of circular scales, length scales, and encoders; • Methods for measuring small lengths, gaps, and distances between two surfaces; • Examples showing the suitability of mechatronic methods for high accuracy correction of machines; and • Particular attention is given to the analysis of ISO written standards of accuracy control, terms and definitions, and methods for evaluation of the measurement results during performance verification.

This book broadens readers'understanding of proactive condition monitoring of low- speed machines in heavy industries. It focuses on why low-speed machines are different than others and how maintenance of these machines should be implemented with particular attention. The authors explain the best available monitoring techniques for various equipment and the principle of how to get proactive information from each technique. They further put forward possible strategies for application of FEM for detection of faults and technical assessment of machinery. Implementation phases are described and industrial case studies of proactive condition monitoring are included. Proactive Condition Monitoring of Low-Speed Machines is an essential resource for engineers and technical managers across a range of industries as well as design engineers working in industrial product development.

With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condition monitoring scheme—thus improving safety and reliability in electric motor operation. It also supplies a solid foundation in the fundamentals of fault cause and effect. Combines Theoretical Analysis and Practical ApplicationWritten by experts in electrical engineering, the book approaches the fault diagnosis of electrical motors through the process of theoretical analysis and practical application. It begins by explaining how to analyze the fundamentals of machine failure using the winding functions method, the magnetic equivalent circuit method, and finite element analysis. It then examines how to implement fault diagnosis using techniques such as the motor current signature analysis (MCSA) method, frequency domain method, model-based techniques, and a pattern recognition scheme. Emphasizing the MCSA implementation method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid vehicles.Fault Modeling, Diagnosis, and Implementation in One VolumeBased on years of research and development at the Electrical Machines & Power Electronics (EMPE) Laboratory at Texas A&M University, this book describes practical analysis and implementation strategies that readers can use in their work. It brings together, in one volume, the fundamentals of motor fault conditions, advanced fault modeling theory, fault diagnosis techniques, and low-cost DSP-based fault diagnosis implementation strategies.

Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as:• fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines.

Are you responsible for your plant's industrial machines? Is My Machine OK? is a compact and handy reference on the potential risks of machine failure and safe machinery operation. Authors Robert Perez and Andre Conkey, aided by several contributors, provide 225 pages in this hardcover guide. It foms a solid basis for reliable and safe machine operations. The authors employ their extensive background to combine the most commonly used assessment tools in a single source. Developed specifically to be taken into the field by machine operators, plant supervisors and maintenance technicians, the guide will also help reliability professionals make informed decisions about factory and process equipment.This book is made up of four sections subdivided into 12 chapters and two appendices:Evaluating Process Machines, which contains basic instruction and practical advice on evaluating the condition of machinesEquipment Specific Assessments, which covers the evaluation of centrifugal pumps, steam turbines, electric motors, and pipingGeneral Assessment Guidelines, which contains field assessment methods, limits, and advice commonly employed to evaluate process machineryImprovement Ideas. Throughout the book there are many relevant examples that will help you better understand the proper application of the various assessment methodologies presented.

Structural health monitoring (SHM) uses one or more in situ sensing systems placed in or around a structure, providing real-time evaluation of its performance and ultimately preventing structural failure. Although most commonly used in civil engineering, such as in roads, bridges, and dams, SHM is now finding applications in other engineering envir

Title from PDF title page (viewed on Sept. 11, 2009).

A woman is operated on while she's awake...A plane runs out of gas while circling an airport for 30 minutes... A passenger liner is mistaken for an enemy fighter and shot down... A company invests in a new system that will cost them money... What do these failure have in common? How can we prevent them from happening again?