Design for diagnosability of multistation manufacturing systems based on sensor allocation optimization

Computers in Industry

Volumn 60, Issue 7, 2009, Pages 501-509

  Sun, Ji wen ^a   Xi, Li feng ^a   Pan, Er shun ^a   Du, Shi chang ^a   Xia, Tang bin ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Design for diagnosability; Dimensional variation; Measurement scheme; Multistation manufacturing system; Sensor allocation

Indexed keywords

ANALYTICAL PROCEDURE; DESIGN FOR DIAGNOSABILITY; DETECTABILITY; DIAGNOSABILITY; DIMENSIONAL VARIATION; DIMENSIONAL VARIATIONS; EARLY DESIGN PHASE; LOCATABILITY; MACHINING PROCESS; MULTI-STATION MANUFACTURING SYSTEMS; MULTISTATION MANUFACTURING SYSTEM; OPTIMAL SENSOR; PERFORMANCE COMPARISON; ROOT CAUSE IDENTIFICATION; SENSING DATA; SENSOR ALLOCATION; SYSTEM MONITORING; TWO-STEP PROCESS;

DESIGN; MACHINING; MANUFACTURE; OPTIMIZATION;

SENSORS;

EID: 67349266186     PISSN: 01663615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compind.2009.02.001     Document Type: Article

References (29)

1. Kurada S., and Bradley C. A review of machine vision sensors for tool condition monitoring. Computers in Industry 34 1 (1997) 55-72
2. Shen L., Tay F.E.H., Qu L., and Shen Y. Fault diagnosis using rough sets theory. Computers in Industry 43 1 (2000) 61-72
3. Yandayan T., and Burdekin M. In-process dimensional measurement and control of workpiece accuracy. International Journal of Machine Tools & Manufacture 37 10 (1997) 1423-1439
4. Thornton A.C. A mathematical framework for the key characteristic process. Research in Engineering Design 11 3 (1999) 145-157
5. Du S.C., Xi L.F., Pan E.S., et al. Modeling and control of dimensional quality of a serial discrete-part machining system. International Journal of Reliability, Quality and Safety Engineering 13 5 (2006) 399-420
6. Do S.B., and Hyung J.Y. Optimal allocation of inspection effort in a serial multi-stage production system. Computers & Industrial Engineering 30 3 (1996) 387-396
7. Wolbrecht E., Dambroio B., and Paasch B. Monitoring and diagnosis of a multi-stage manufacturing process using Bayesian networks. Artificial Intelligence for Engineering Design & Manufacturing 14 (1994) 53-67
8. Guan J., and Graham J.H. An integrated approach for fault diagnosis with learning. Computers in Industry 32 1 (1996) 33-51
9. Wiele H., Liermann G., and Raschke H. Monitoring, diagnosis and operator leading systems for flexible automated manufacturing. Computers in Industry 10 1 (1988) 61-68
10. Liu J., Luo Z., Chu L., et al. Manufacturing system design with optimal diagnosability. International Journal of Production Research 42 9 (2004) 1695-1714
11. Xia Q., and Rao M. Actuator and sensor design for operation support systems. Computers in Industry 39 2 (1999) 79-95
12. Ding Y., Shi J., and Ceglarek D. Diagnosability analysis of multi-station manufacturing processes. Journal of Dynamic System Measurement and Control 124 11 (2002) 1-13
13. Pearson D., Pillai S., and Lee Y. An algorithm for near-optimal placement of sensor elements. IEEE Transactions on Informatics Theory 36 (1990) 1280-1284
14. Shah P., and Udwadia F. A methodology for optimal sensor locations for identification of dynamic systems. Journal of Applied Mechanics 45 (1978) 188-196
15. Longman R.W., and Juang J. System identification via eigensystem realization. Journal of Guide, Control Dynamics 12 (1989) 647-652
16. Cameron A., and Durrant-Whyte H.A. Bayesian approach to optimal sensor placement. International Journal of Robotics Research 9 (1990) 70-88
17. Kammer D. Sensor placement for on-orbit model identification and correlation of large space structures. Journal of Guide, Control, Dynamics 14 (1991) 252-259
18. Lim T. Sensor placement for on-orbit modal testing. Journal of Spacer Rockets 29 (1992) 239-246
19. Hac A., and Liu L. Sensor and actuator location in motion control of flexible structures. Journal of Sound Vibration 167 (1993) 239-261
20. Yao L., Sethares W., and Kammer D. Sensor placement for on-orbit modal identification via a genetic algorithm. AIAA Journal 31 (1993) 1922-1928
21. Yan Y. Sensor placement and diagnosability analysis at design stage. Proceedings of 16th European Conference on Artificial Intelligence, MONET Workshop on Model-Based Systems (2004)
22. Camelio J.A., Hu S.J., and Yim H. Sensor placement for effective diagnosis of multiple faults in fixturing of compliant parts. Journal of Manufacturing Science and Engineering 127 1 (2005) 68-74
23. Khan A., and Ceglarek D. Sensor optimization for fault diagnosis in multi-fixture assembly systems with distributed sensing. Journal of Manufacturing Science and Engineering 122 1 (2000) 215-226
24. Mehrabi M.G., Ulsoy A.G., and Koren Y. Reconfigurable manufacturing systems: key to future manufacturing. Journal of Intelligent Manufacturing 11 4 (2000) 403-419
25. Guinea D., Ruiz A., and Barrios L.J. Multi-sensor integration-an automatic feature selection and state identification methodology for tool wear estimation. Computers in Industry 17 2-3 (1991) 121-130
26. Worden K., and Burrows A. Optimal sensor placement for fault detection. Engineering Structures 23 (2001) 885-901
27. Bagajewicz M., and Cabrera E. New MILP formulation for instrumentation network design and upgrade. American Institute of Chemical Engineering Journal 48 10 (2002) 2271-2282
28. Du S.C., Xi L.F., and Shi J. Distributed sensor system for fault detection and isolation in multistage manufacturing systems. International Journal of Computer Applications in Technology 25 4 (2006) 182-191
29. Lee J. Measurement of machine performance degradation using a neural network model. Computers in Industry 30 3 (1996) 193-209