Computer-aided Maintenance

1. Front Matter

   Pages i-xiii

   PDF

2. Methodologies

   1. Front Matter

      Pages 1-1

      PDF

   2. Fundamentals of maintenance

      • Gerald M. Knapp, Ben Wang

      Pages 3-18

   3. Fundamentals of sensory systems for maintenance engineering

      • Jay Lee, John Tsai, John Fildes

      Pages 19-40

   4. Related work on machine monitoring and diagnostics

      • Hsin Hao Huang, Ben Wang

      Pages 41-58

   5. Parametric modeling methods: theory and a case study

      • Julie Spoerre, Ben Wang

      Pages 59-85

   6. Machine performance estimation and reliability modeling

      • Chang-Ching Lin, Ben Wang

      Pages 86-116

   7. Design methodology for self-maintenance machines

      • Yasushi Umeda, Tetsuo Tomiyama, Tomohiko Sakao, Yoshiki Shimomura

      Pages 117-135

   8. Integrated prognostics, maintenance and life-extending control of continuous-time production processes

      • Asok Ray, Shashi Phoha

      Pages 136-169

   9. An integrated automated root cause identification fuzzy neural network reasoning for quality control

      • Farhad Tadayon, Jay Lee

      Pages 170-180

   10. Maintenance using activity-based costing (ABC)

       • Alice S. Tsai

       Pages 181-208

   11. Life cycle maintenance management

       • Shozo Takata

       Pages 209-230

   12. Life extension of operating machinery using the National Information Infrastructure (NII)

       • Shashi Phoha, Asok Ray

       Pages 231-260

3. Case Examples

   1. Front Matter

      Pages 261-261

      PDF

   2. Case Example 1: Motor incipient fault detection using artificial neural network and fuzzy logic technologies

      • Mo-yuen Chow, Yuan-Shin Lee, H. Joel Trussell

      Pages 263-280

   3. Case Example 2: Data analysis for diagnostics and process monitoring of automotive engines

      • Bruce D. Bryant, Kenneth A. Marko

      Pages 281-301

   4. Case Example 3: Measurement of machine performance degradation using a neural network model

      • Jay Lee

      Pages 302-317

   5. Case Example 4: Detection and isolation of faults in the stamping process using the Haar transform

      • Christopher K. H. Koh, William J. Williams

      Pages 318-338

   6. Case Example 5: Fault monitoring in manufacturing systems using template models

      • Lawrence E. Holloway

      Pages 339-355

   7. Case Example 6: In-process diagnosis of tool failures in milling

      • Kazuo Mori

      Pages 356-378

In today's business environment, reliability and maintenance drastically affect the three key elements of competitiveness - quality, cost, and product lead time. Well-maintained machines hold tolerances better, help reduce scrap and rework, and raise consistency and quality of the part in addition to cutting total production costs. Today, many factories are still performing maintenance on equipment in a reactive manner due to a lack of understanding about machine performance behaviour. To improve production efficiency, computer-aided maintenance and diagnostic methodology must be applied effectively in manufacturing. This book focuses on the fundamental principles of predictive maintenance and diagnostic engineering. In addition to covering the relevant theory, techniques and methodologies in maintenance engineering, the book also provides numerous case studies and examples illustrating the successful application of the principles and techniques outlined.

• Manufacturing
• Manufacturing System
• Motor
• Sensor
• design
• environment
• fuzzy
• logic
• management
• modeling
• quality

• National Science Foundation, USA

  Jay Lee

• FAMU-FSU College of Engineering, USA

  Ben Wang

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