Classification of Wear State for a Positive Displacement Pump Using Deep Machine Learning PDF Download
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Author: Jarosław Konieczny
Publisher: Infinite Study
ISBN:
Category : Computers
Languages : en
Pages : 19
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Book Description
Hydraulic power systems are commonly used in heavy industry (usually highly energy intensive and are often associated with high power losses. Designing a suitable system to allow an early assessment of the wear conditions of components in a hydraulic system (e.g., an axial piston pump) can effectively contribute to reducing energy losses during use. This paper presents the application of a deep machine learning system to determine the efficiency state of a multi-piston positive displacement pump. Such pumps are significant in high-power hydraulic systems. The correct operation of the entire hydraulic system often depends on its proper functioning. The wear and tear of individual pump components usually leads to a decrease in the pump’s operating pressure and volumetric losses, subsequently resulting in a decrease in overall pump efficiency and increases in vibration and pump noise. This in turn leads to an increase in energy losses throughout the hydraulic system, which releases excess heat. Typical failures of the discussed pumps and their causes are described after reviewing current research work using deep machine learning. Next, the test bench on which the diagnostic experiment was conducted and the selected operating signals that were recorded are described. The measured signals were subjected to a time–frequency analysis, and their features, calculated in terms of the time and frequency domains, underwent a significance ranking using the minimum redundancy maximum relevance (MRMR) algorithm. The next step was to design a neural network structure to classify the wear state of the pump and to test and evaluate the effectiveness of the network’s recognition of the pump’s condition. The whole study was summarized with conclusions.
Author: Jarosław Konieczny
Publisher: Infinite Study
ISBN:
Category : Computers
Languages : en
Pages : 19
Get Book Here
Book Description
Hydraulic power systems are commonly used in heavy industry (usually highly energy intensive and are often associated with high power losses. Designing a suitable system to allow an early assessment of the wear conditions of components in a hydraulic system (e.g., an axial piston pump) can effectively contribute to reducing energy losses during use. This paper presents the application of a deep machine learning system to determine the efficiency state of a multi-piston positive displacement pump. Such pumps are significant in high-power hydraulic systems. The correct operation of the entire hydraulic system often depends on its proper functioning. The wear and tear of individual pump components usually leads to a decrease in the pump’s operating pressure and volumetric losses, subsequently resulting in a decrease in overall pump efficiency and increases in vibration and pump noise. This in turn leads to an increase in energy losses throughout the hydraulic system, which releases excess heat. Typical failures of the discussed pumps and their causes are described after reviewing current research work using deep machine learning. Next, the test bench on which the diagnostic experiment was conducted and the selected operating signals that were recorded are described. The measured signals were subjected to a time–frequency analysis, and their features, calculated in terms of the time and frequency domains, underwent a significance ranking using the minimum redundancy maximum relevance (MRMR) algorithm. The next step was to design a neural network structure to classify the wear state of the pump and to test and evaluate the effectiveness of the network’s recognition of the pump’s condition. The whole study was summarized with conclusions.
Author: John Watton
Publisher: Springer Science & Business Media
ISBN: 1846283744
Category : Technology & Engineering
Languages : en
Pages : 367
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Book Description
This book covers the background theory of fluid power and indicates the range of concepts needed for a modern approach to condition monitoring and fault diagnosis. The theory is leavened by 15-years-worth of practical measurements by the author, working with major fluid power companies, and real industrial case studies. Heavily supported with examples drawn from real industrial plants – the methods in this book have been shown to work.
Author: Ken Skorseth
Publisher:
ISBN:
Category : Gravel roads
Languages : en
Pages : 112
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Book Description
The purpose of this manual is to provide clear and helpful information for maintaining gravel roads. Very little technical help is available to small agencies that are responsible for managing these roads. Gravel road maintenance has traditionally been "more of an art than a science" and very few formal standards exist. This manual contains guidelines to help answer the questions that arise concerning gravel road maintenance such as: What is enough surface crown? What is too much? What causes corrugation? The information is as nontechnical as possible without sacrificing clear guidelines and instructions on how to do the job right.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 140
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Book Description
Popular Mechanics inspires, instructs and influences readers to help them master the modern world. Whether it’s practical DIY home-improvement tips, gadgets and digital technology, information on the newest cars or the latest breakthroughs in science -- PM is the ultimate guide to our high-tech lifestyle.
Author: Charles R. Farrar
Publisher: John Wiley & Sons
ISBN: 1118443217
Category : Technology & Engineering
Languages : en
Pages : 735
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Book Description
Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions. Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies. Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms Benefits from extensive use of the authors’ detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.
Author: Donald Michie
Publisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 312
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Book Description
Author: K. L. Narayana
Publisher: New Age International
ISBN: 8122419178
Category : Machine design
Languages : en
Pages : 10
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Book Description
About the Book: Written by three distinguished authors with ample academic and teaching experience, this textbook, meant for diploma and degree students of Mechanical Engineering as well as those preparing for AMIE examination, incorporates the latest st
Author: Johann Friedrich Gülich
Publisher: Springer
ISBN: 3642401147
Category : Technology & Engineering
Languages : en
Pages : 1146
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Book Description
This book gives an unparalleled, up-to-date, in-depth treatment of all kinds of flow phenomena encountered in centrifugal pumps including the complex interactions of fluid flow with vibrations and wear of materials. The scope includes all aspects of hydraulic design, 3D-flow phenomena and partload operation, cavitation, numerical flow calculations, hydraulic forces, pressure pulsations, noise, pump vibrations (notably bearing housing vibration diagnostics and remedies), pipe vibrations, pump characteristics and pump operation, design of intake structures, the effects of highly viscous flows, pumping of gas-liquid mixtures, hydraulic transport of solids, fatigue damage to impellers or diffusers, material selection under the aspects of fatigue, corrosion, erosion-corrosion or hydro-abrasive wear, pump selection, and hydraulic quality criteria. As a novelty, the 3rd ed. brings a fully analytical design method for radial impellers, which eliminates the arbitrary choices inherent to former design procedures. The discussions of vibrations, noise, unsteady flow phenomena, stability, hydraulic excitation forces and cavitation have been significantly enhanced. To ease the use of the information, the methods and procedures for the various calculations and failure diagnostics discussed in the text are gathered in about 150 pages of tables which may be considered as almost unique in the open literature. The text focuses on practical application in the industry and is free of mathematical or theoretical ballast. In order to find viable solutions in practice, the physical mechanisms involved should be thoroughly understood. The book is focused on fostering this understanding which will benefit the pump engineer in industry as well as academia and students.
Author: Kang Li
Publisher: Springer
ISBN: 3662452863
Category : Computers
Languages : en
Pages : 570
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Book Description
This book constitutes the third part of the refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2014, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014, held in Shanghai, China, in September 2014. The 159 revised full papers presented in the three volumes of CCIS 461-463 were carefully reviewed and selected from 572 submissions. The papers of this volume are organized in topical sections on computational intelligence in utilization of clean and renewable energy resources, including fuel cell, hydrogen, solar and winder power, marine and biomass; intelligent modeling, control and supervision for energy saving and pollution reduction; intelligent methods in developing electric vehicles, engines and equipment; intelligent computing and control in distributed power generation systems; intelligent modeling, simulation and control of power electronics and power networks; intelligent road management and electricity marketing strategies; intelligent water treatment and waste management technologies; integration of electric vehicles with smart grid.
Author: Raymond S Beebe
Publisher: Elsevier
ISBN: 0080514642
Category : Technology & Engineering
Languages : en
Pages : 193
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Book Description
This book shows how condition monitoring can be applied to detect internal degradation in pumps so that appropriate maintenance can be decided upon based on actual condition rather than arbitrary time scales. The book focuses on the main condition monitoring techniques particularly relevant to pumps (vibration analysis, performance analysis). The philosophy of condition monitoring is briefly summarised and field examples show how condition monitoring is applied to detect internal degration in pumps.* The first book devoted to condition monitoring and predictive maintenance in pumps. * Explains how to minimise energy costs, limit overhauls and reduce maintenance expenditure.* Includes material not found anywhere else.
