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Author : R. J. Mullen
Publisher :
Page : 10 pages
File Size : 41,48 MB
Release :
Category : Systems engineering
ISBN :
Author : Mohammed Hamed Ahmed Soliman
Publisher : Mohammed Hamed Ahmed Soliman
Page : 224 pages
File Size : 49,19 MB
Release : 2020-11-03
Category : Business & Economics
ISBN :
Condition monitoring is the process of keeping an eye on a machine's condition parameter in order to spot any major changes that could be signs of a malfunction developing. It plays a significant role in preventive maintenance and is a major component of predictive maintenance. By combining machine sensor data that detects vibration and other characteristics (in real-time) with cutting-edge machine monitoring software, condition monitoring (CM), a maintenance strategy, anticipates machine health and safety. Predictive Maintenance strategy employs vibration analysis, thermography analysis, ultrasound analysis, oil analysis and other techniques to improve machine reliability. The goal of the strategy is to provide the stated function of the facility, with the required reliability and availability at the lowest cost.
Author : Raymond S Beebe
Publisher : Elsevier
Page : 200 pages
File Size : 38,93 MB
Release : 2004-04-16
Category : Business & Economics
ISBN : 9781856174084
Condition monitoring and its part in maintenance, pump performance and the effect of water, performance analysis and testing of pumps for condition conitoring, performance analysis and its application to optimise time for overhaul, other methods of performance analysis for pump condition monitoring, vibration anaysis of pumps -- basic, vibration analysis of pumps -- advanced methos, other uses of condition monitoring information, other condition monitoring methods, positive displacement pumps, case studies in condition monitoring of pumps.
Author : Edwin Lughofer
Publisher : Springer
Page : 567 pages
File Size : 49,76 MB
Release : 2019-02-28
Category : Technology & Engineering
ISBN : 3030056457
This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.
Author : Paresh Girdhar
Publisher : Newnes
Page : 255 pages
File Size : 40,71 MB
Release : 2004
Category : Science
ISBN : 9780750662758
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. 1. Understand the basics of vibration measurement 2. Apply vibration analysis for different machinery faults 3. Diagnose machinery-related problems with vibration analysis techniques
Author : Cornelius Scheffer
Publisher : Elsevier
Page : 263 pages
File Size : 21,19 MB
Release : 2004-07-16
Category : Technology & Engineering
ISBN : 0080480225
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
Author : B. K. N. Rao
Publisher : Elsevier
Page : 862 pages
File Size : 25,82 MB
Release : 1996
Category : Business & Economics
ISBN : 9781856172349
Hardbound. The need to reduce costs has generated a greater interest in condition monitoring in recent years. The Handbook of Condition Monitoring gives an extensive description of available products and their usage making it a source of practical guidance supported by basic theory.This handbook has been designed to assist individuals within companies in the methods and devices used to monitor the condition of machinery and products.
Author : R. Keith Mobley
Publisher : Elsevier
Page : 451 pages
File Size : 46,75 MB
Release : 2002-10-24
Category : Technology & Engineering
ISBN : 0080478697
This second edition of An Introduction to Predictive Maintenance helps plant, process, maintenance and reliability managers and engineers to develop and implement a comprehensive maintenance management program, providing proven strategies for regularly monitoring critical process equipment and systems, predicting machine failures, and scheduling maintenance accordingly. Since the publication of the first edition in 1990, there have been many changes in both technology and methodology, including financial implications, the role of a maintenance organization, predictive maintenance techniques, various analyses, and maintenance of the program itself. This revision includes a complete update of the applicable chapters from the first edition as well as six additional chapters outlining the most recent information available. Having already been implemented and maintained successfully in hundreds of manufacturing and process plants worldwide, the practices detailed in this second edition of An Introduction to Predictive Maintenance will save plants and corporations, as well as U.S. industry as a whole, billions of dollars by minimizing unexpected equipment failures and its resultant high maintenance cost while increasing productivity. A comprehensive introduction to a system of monitoring critical industrial equipment Optimize the availability of process machinery and greatly reduce the cost of maintenance Provides the means to improve product quality, productivity and profitability of manufacturing and production plants
Author : Edwin Lughofer
Publisher :
Page : pages
File Size : 13,79 MB
Release : 2019
Category : Electronic books
ISBN : 9783030056469
This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power. .
Author : Mohammed Abdul Qawi Zaid
Publisher :
Page : 172 pages
File Size : 49,86 MB
Release : 2014
Category : Shock (Mechanics)
ISBN :
A machine in the best of operating condition will have some vibration because of small, minor defects. The use of the human sense of touch and feel for observation is somewhat limited, and there are many common problems that are generally out of the range of human senses. Vibration monitoring is a widely used and cost effective monitoring technique. It detects, locates, and distinguishes faults in rotating machineries. It is an established process used in predictive maintenance as it is necessary to diagnose faults in machine at early stages to prevent failure during operation. In this research an intelligent method to detect faults in rotating machineries by analyzing vibration signals was developed. The faults that can be detected are some of the most common faults in rotating machineries. An experimental set-up was designed and fabricated to observe the signals generated when it is in normal working condition and when it is in faulty condition. The components whose vibration signatures were observed are rotor disk and motor. The faulty rotor disk, mechanical looseness, and fault motor vibration signatures were studied. Four features from vibration signals for various faults were extracted in the time domain. They are Root Mean Square (RMS), crest factor, kurtosis, and skewness. These features are mapped against the respective faults using a multilayer feed forward artificial neural network. The network was trained using Levenberg-Marquardt algorithm. The simulated faults condition signal were analyzed and compared to normal condition signals. The analysis of the fault signature shows that fault conditions in the system are detected for the various components. In this research, the developed artificial neural network is able to detect the faulty conditions. The trained neural network can classify different condition with 92.5% accuracy and the precision is 0.9. For further research, it is suggested that the artificial neural network be trained to detect more inherent faults in the system components.