Computer-aided Maintenance

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.