Computer-aided Maintenance

Manufacturing Systems Engineering Series

Series editor: Hamid R. Parsaei, Department of Industrial Engineering, University of Louisville, USA

The globalization of business and industry and the worldwide competitive economy are forcing business leaders in the manufacturing and service sectors to utilize fully the best equipment and techniques available. The objective is to have efficient control of the organizational structure in order to produce high quality products at lower prices within a shorter period of time.

Since the introduction of computers in the 1950s, Manufacturing Systems Engineering has experienced tremendous growth. The development of the discipline has helped industry to become more productive and to make more efficient use of resources. Manufacturing information systems, total quality management, facility layout, material handling, value engineering and cost analysis, safety, computer­integrated manufacturing, and production planning and shop floor control are just some of the areas in which manufacturing systems engineers have been traditionally involved in order to help improve understanding and awareness in the manufacturing and service sectors. The recent emphasis and concern about the environment and product recyclability and re-usability have brought new perspectives and more challenges to this ever-growing engineering discipline.

The aim of the Manufacturing Systems Engineering Series is to provide an outlet for state-of-the-art topics in manufacturing systems engineering. This series is also intended to provide a scientific and practical basis for researchers, practitioners and students involved in manufacturing systems areas. Issues which are addressed in this series include, but are not limited to, the following:

• Production system design and control • Life cycle analysis • Simulation in manufacturing • Manufacturing cost estimating • Industrial safety • Fuzzy logic and neural networks in manufacturing • CAD/CAM/CIM

We would welcome proposals to write material for this series from colleagues and industry leaders around the world. We hope that researchers both in academia and government, as well as private

organizations and individual practitioners, will find this series informative and worthwhile.

1 Manufacturing Decision Support Systems Edited by Hamid R. Parsaei, Sai Kolli and Thomas R. Hanley

2 Integrated Product, Process and Enterprise Design Edited by Ben Wang

3 Occupational Ergonomics Fariborz Tayyari and James L. Smith

4 Rapid Response Manufacturing Edited by Jian Dong

Computer-aided Maintenance

Methodologies and Practices

Edited by

Jay Lee National Science Foundation

Virginia USA

and

Ben Wang F AMU-FSU College of Engineering

Florida USA

SPRINGER SCIENCE+BUSINESS MEDIA, B.V.

A c.I.P. Catalogue record for this book is available from the Library of Congress A catalogue record for this book is available from the British Library

ISBN 978-1-4613-7421-3 ISBN 978-1-4615-5305-2 (eBook) DOI 10.1007/978-1-4615-5305-2

Printed an acid-free paper

AH rights reserved © 1999 Springer Science+Business Media Dordrecht

Origina1ly published by Kluwer Academic in 1999 Softcover reprint of the hardcover Ist edition 1999

No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical,

including photocopying, recording or by any information storage and retrieval system, without written permission from the copyright owner.

Typeset in 10/12 Palatino by Cambrian Typesetters, Frimley, Surrey, UK

Contents

List of contributors Preface

ix Xl

PART ONE Methodologies 1

1. Fundamentals of maintenance 3 Gerald M. Knapp and Ben Wang

2. Fundamentals of sensory systems 19 for maintenance engineering Jay Lee, John Tsai and John Fildes

3. Related work on machine monitoring 41 and diagnostics Hsin Hao (Tom) Huang and Ben Wang

4. Parametric modeling methods: 59 theory and a case study Julie Spoerre and Ben Wang

5. Machine performance estimation 86 and reliability modeling Chang-Ching Lin and Ben Wang

6. Design methodology for self-maintenance 117 machines Yasushi Umeda, Tetsuo Tomiyama, Tomohiko Sakao and Yoshiki Shimomura

7. Integrated prognostics, maintenance and 136 life-extending control of continuous-time production processes Asok Ray and Shashi Phoha

8. An integrated automated root cause 170 identification fuzzy neural network reasoning for quality control Farhad Tadayon and Jay Lee

9. Maintenance using activity-based costing (ABC) 181 Alice S. Tsai

viii Contents

10. Life cycle maintenance management 209 Shozo Takata

11. Life extension of operating machinery using the 231 National Information Infrastructure (Nil) Shashi Phoha and Asok Ray

PART TWO Case Examples 261

12. Case Example 1: Motor incipient fault 263 detection using artificial neural network and fuzzy logic technologies Mo-yuen Chow, Yuan-Shin Lee and H. Joel Trussell

13. Case Example 2: Data analysis for diagnostics 281 and process monitoring of automotive engines Bruce D. Bryant and Kenneth A. Marko

14. Case Example 3: Measurement of machine 302 performance degradation using a neural network model Jay Lee

15. Case Example 4: Detection and isolation 318 of faults in the stamping process using the Haar transform Christopher K.H. Koh and William J. Williams

16. Case Example 5: Fault monitoring in 339 manufacturing systems using template models Lawrence E. Holloway

17. Case Example 6: In-process diagnosis of 356 tool failures in milling KazuoMori

18. Case Example 7: Monitoring and predicting 379 surface roughness and bore tolerance in end-milling A. Chukwujekwu Okajor

Index 407

Contributors

Bruce Bryant Ford Research Laboratory, Ford Motor Company, Dearborn, MI, USA

Mo-Yuen Chow Department of Electrical and Computer Science, North Carolina State University, Box 7911, Raleigh, NC 27695, USA

Hsin-Hao (Tom) Huang Industrial Engineering, National Yunlin Polytechnic Institute, 64 Wun-Hua Road, Hu-Wei, Taiwan.

Lawrence Holloway Center for Manufacturing Systems, 414F CRMS Building, University of Kentucky, Lexington, KY 40506-0108 USA

Gerald M. Knapp Maintenance and Reliability Research Unit, Industrial and Manufacturing Systems Engineering, 3128 CEBA Building, Louisiana State Univesity, Baton Rouge, LA 79893, USA

Christopher Kok-Hwee Koh School of Mechanical and Production Engineering, Nantang Technological University, Nanyang Avenue, Singapore 639798

Jay Lee National Science Foundation, 4201 Wilson Boulevard, Room 585, Arlington, VA 22230, USA

Yuan-Shin Lee Department of Industrial Engineering, North Carolina State University, Raleigh, NC 27695-7911, USA

Chang-Ching Lin Department of Industrial Engineering and Management, St John's and St Mary's Institute of Technology, 499, Sec. 4, Tam-King Road, Tamshui, Taipei County, Taiwan

Kenneth Marko Ford Research Laboratory, Ford Motor Company, Dearborn, MI, USA

Kazuo Mori Department of Manufacturing Systems, Mechanical Engineering Laboratory, Agency of Industrial Science and Technology, MITI, Namiki 1-2, Tsukuba City, Ibaraki 305, Japan

Anthony Okafor Department of Mechanical Engineering, University of Missouri-Rolla, Rolla, MO 65409, USA

x Contributors

Shashi Phoha Information Systems, Applied Research Laboratory, PO Box 30, Penn State University, State College, PS 16804, USA

Asok Ray Mechanical Engineering Department, The Pennsylvania State University, University Park, PA 16802, USA

Tomohiko Sakao Department of Precision Machinery Engineering, The Graduate School of Engineering, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113, Japan

Yoshiki Shimomura Mita Industrial Co. Ltd, Tamatsukuri 1-2-28, Chuo­ku, Osaka, Japan

Julie K. Spoerre College of Engineering, Southern Illinois University, Mailcode 6603, Carbondale, IL 62901, USA

Farhad Tadayon Technology Assistance Center, College of Technology, 2409 Scanlan Avenue, Salina, Kansas 67401-8196, USA

Shozo Takata Department of Industrial Engineering, Waseda University, Okubo 3-4-1, Shinjuku-ku, Tokyo 169, Japan

Tetsuo Tomiyama Department of Precision Machinery Engineering, The Graduate School of Engineering, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113, Japan

H. Joel Trussel Department of Mechanical and Computer Engineering, North Carolina State University, Raleigh, NC 27695-7911, USA

Alice Tsai 7400 S. State St, Apt 13303, Midvale, UT 84047 USA

John Tsai 7400 S. State St, Apt 13303, Midvale, UT 84947 USA

Yasushi Umeda Department of Precision Machinery Engineering, The Graduate School of Engineering, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113, Japan

Ben Wang Department of Industrial Engineering, FSU/FAMU, 2525 Pottsdamer Street, Tallahassee, FL 32310-6046, USA

William J. Williams Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109-2122, USA

Preface

Equipment reliability and maintenance drastically affect the three key elements of competitiveness - quality, cost, and product lead time. Well-maintained equipment holds tolerances better, helps reduce scrap and rework, and improves consistency and quality of the part. The recent rush to embrace advanced sensors and control systems in modern equipment has further increased the use of relatively unknown and untested technology. Typically, when a production system goes down, only a small fraction of the downtime is spent repairing the equipment that causes the failure. A substantial part of the downtime is spent on locating the source of the problems. Difficulty in identifying the causes of system failures has been attributed to several factors, including system complexity and a lack of knowledge and adequate troubleshooting tools.

This book introduces modern theories, techniques and tools such as artificial intelligence, expert systems and neural networks for effective maintenance engineering. In addition, it covers recent advances in reliability, design for self-maintenance machines, digital condition monitoring, signature analysis, and life cycle engineering. Two issues regarding computer-aided maintenance are addressed: the first issue is the fundamental knowledge, including both theories and methodologies required for practitioners to perform maintenance activities effectively. With the revolutionary advances in information, telecommunication, and computing technologies, we forsee needs for new approaches in design, process, and the use of materials, technologies, and human resources in the modern factory; the second and equally pressing issue is to understand how to implement the right maintenance tools and techniques, based on the introduced theories and methodologies, to solve problems in a very short time frame in order to guarantee success.

The book is divided into two parts with a total of 18 chapters. Part One (Chapters 1 to 11) of the book introduces new methodologies which lay a foundation for readers to understand the recent advances in maintenance techniques and methods; Part Two (Chapters 12 to 18) provides readers with 'how to know' knowledge and practices by illustrating the introduced methodologies using seven case studies. Chapter 1 introduces the fundamentals of maintenance, including the

xii Preface

definitions of preventative maintenance, predictive maintenance, deterioration process and machine diagnostics. Chapter 2 introduces fundamental sensory systems related to maintenance engineering, including sensors, signal processing, sensor fusion, and operation algorithms. Chapter 3 builds upon Chapter 2's foundation and addresses applied sensory systems for machine monitoring and diagnostics, including the use of expert systems and an artificial networks system for maintenance practices. Chapter 4 focuses on the parameter modeling which deals with the exponentially weighted moving average (EWMA) model for monitoring a process standard deviation as well as the autoregressive (AR) model for the estimation of process parameters. Chapter 5 emphasizes the reliability modeling for machine performance including machine performance degradation modeling and fault detection. Chapter 6 introduces the design methodology for self-maintenance machines, including the systematic understanding of fault monitoring, fault recognition, repamng planning, and self-recovery strategy. A particular example of a self­maintenance copier is examined to illustrate the design practices. Chapter 7 addresses an integrated prognostics, maintenance, and life­extending control of continuous-time production processes. A methodology dealing with condition-based maintenance for a complex continuous system is introduced. Chapter 8 introduces a methodology for root cause identification using a fuzzy neural network approach. A Boeing door assembly process system is used to explain the applied quality control practices. Chapter 9 illustrates maintenance using activity-based costing (ABC) for the system justification of deployment of a modern maintenance system. Chapter 10 introduces a life cycle maintenance management system. Chapter 11 examines life extension of operating machinery using the national information infrastructure (NIl). Chapters 12 to 18 demonstrate the implementation of the introduced methodologies in the previous chapters for a number of applications, including motor fault detection, automotive engines diagnostics, machine degradation monitoring, tool failure monitoring in milling, fault detection and isolation in the stamping process, fault monitoring in a manufacturing system, and monitoring of surface roughness and bore tolerance in end-milling.

In summary, this book takes a user-oriented, hands-on approach for practising engineers or managers to develop initial expertise in computer-aided maintenance methodologies. It is hoped that Computer­aided Maintenance: Methodologies and Practices will bridge the gap for industrial users, eventually making world-class maintenance a requirement for modern factories to remain competitive and productive, as more and more companies embrace the modern computer-aided maintenance methodologies.

The editors wish to thank the authors and reviewers who made this

Preface xiii

book a reality. The editors also express their appreciation to Dr Hamid R. Parsaei, the Series Editor, for the opportunity to edit the book.

Jay Lee Ben Wang