Computer-aided Maintenance

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.

Contenu
One Methodologies.- 1. Fundamentals of maintenance.- 2. Fundamentals of sensory systems for maintenance engineering.- 3. Related work on machine monitoring and diagnostics.- 4. Parametric modeling methods: theory and a case study.- 5. Machine performance estimation and reliability modeling.- 6. Design methodology for self-maintenance machines.- 7. Integrated prognostics, maintenance and life-extending control of continuous-time production processes.- 8. An integrated automated root cause identification fuzzy neural network reasoning for quality control.- 9. Maintenance using activity-based costing (ABC).- 10. Life cycle maintenance management.- 11. Life extension of operating machinery using the National Information Infrastructure (NII).- Two Case Examples.- 12. Case Example 1: Motor incipient fault detection using artificial neural network and fuzzy logic technologies.- 13. Case Example 2: Data analysis for diagnostics and process monitoring of automotive engines.- 14. Case Example 3: Measurement of machine performance degradation using a neural network model.- 15. Case Example 4: Detection and isolation of faults in the stamping process using the Haar transform.- 16. Case Example 5: Fault monitoring in manufacturing systems using template models.- 17. Case Example 6: In-process diagnosis of tool failures in milling.- 18. Case Example 7: Monitoring and predicting surface roughness and bore tolerance in end-milling.