The purpose of this book is to serve as a reference text for the maintenance engineer and technician who is working with condition monitoring and predictive machinery maintenance technology. Broadly speaking, the subject is the principles of vibration theory and analysis as they apply to the determination of machine operating characteristics and deficiencies. The first chapter underscores the importance of vibration analysis in the field of predictive maintenance and root cause failure analysis.The chapters on vibration theory and frequency analysis lay the groundwork for the chapter on machine fault diagnostics based on vibration measurement and analysis. A systematic approach is used here to guide the reader through a logical sequence of steps to determine a machine's condition by detailed examination of vibration signatures.

Machinery Vibration Analysis and Predictive Maintenance provides a detailed examination of the detection, location and diagnosis of faults in rotating and reciprocating machinery using vibration analysis. The basics and underlying physics of vibration signals are first examined. The acquisition and processing of signals is then reviewed followed by a discussion of machinery fault diagnosis using vibration analysis. Hereafter the important issue of rectifying faults that have been identified using vibration analysis is covered. The book also covers the other techniques of predictive maintenance such as oil and particle analysis, ultrasound and infrared thermography. The latest approaches and equipment used together with the latest techniques in vibration analysis emerging from current research are also highlighted. - Understand the basics of vibration measurement - Apply vibration analysis for different machinery faults - Diagnose machinery-related problems with vibration analysis techniques

Vibration Problems in Machines explains how to infer information about the internal operations of rotating machines from external measurements through methods used to resolve practical plant problems. Second edition includes summary of instrumentation, methods for establishing machine rundown data, relationship between the rundown curves and the ideal frequency response function. The section on balancing has been expanded and examples are given on the strategies for balancing a rotor with a bend, with new section on instabilities. It includes case studies with real plant data, MATLAB® scripts and functions for the modelling and analysis of rotating machines.

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.

This book opens with an explanation of the vibrations of a single degree-of-freedom (dof) system for all beginners. Subsequently, vibration analysis of multi-dof systems is explained by modal analysis. Mode synthesis modeling is then introduced for system reduction, which aids understanding in a simplified manner of how complicated rotors behave. Rotor balancing techniques are offered for rigid and flexible rotors through several examples. Consideration of gyroscopic influences on the rotordynamics is then provided and vibration evaluation of a rotor-bearing system is emphasized in terms of forward and backward whirl rotor motions through eigenvalue (natural frequency and damping ratio) analysis. In addition to these rotordynamics concerning rotating shaft vibration measured in a stationary reference frame, blade vibrations are analyzed with Coriolis forces expressed in a rotating reference frame. Other phenomena that may be assessed in stationary and rotating reference frames include stability characteristics due to rotor internal damping and instabilities due to asymmetric shaft stiffness and thermal unbalance behavior.

A comprehensive treatment of "linear systems analysis" applied to dynamic systems as an approach to interdisciplinary system design beyond the related area of electrical engineering. The text gives an interpretation of mechanical vibrations based on the theory of dynamic systems, aiming to bridge the gap between existing theoretical methods in different engineering disciplines and to enable advanced students or professionals to model dynamic and vibrating systems with reference to communication and control processes. Emphasizing the theory it presents a balanced coverage of analytical principles and applications to vibrations with regard to mechatronic problems.

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.