Machinery Condition Monitoring
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Condition Monitoring with Vibration Signals
Author: Hosameldin Ahmed
language: en
Publisher: John Wiley & Sons
Release Date: 2020-01-07
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.
Machinery Condition Monitoring
This book introduces the engineer to techniques of detection and diagnosis of faults occurring in machines in general and rotating machines. It presents all methods of fault detection machines, and includes a brief review of vibrational analysis and rotor dynamics, followed by techniques of wear and debris analysis. It provides other techniques of machinery condition monitoring such as the NDT techniques and thermography. The book also contains many case studies.
Mechanical Vibrations and Condition Monitoring
Author: Juan Carlos A. Jauregui Correa
language: en
Publisher: Academic Press
Release Date: 2020-03-04
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.