Application of Bayesian networks for diagnostics in the assembly process by considering small measurement data sets

International Journal of Advanced Manufacturing Technology

Volumn 65, Issue 9-12, 2013, Pages 1229-1237

  Liu, Yinhua ^a   Jin, Sun ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Assembly deviation; Bayesian network; Fault diagnosis; Process control

Indexed keywords

ASSEMBLY; AUTOMOTIVE INDUSTRY; FAILURE ANALYSIS; FAULT DETECTION; FIXTURES (TOOLING); KNOWLEDGE BASED SYSTEMS; PROBABILITY DISTRIBUTIONS; PROCESS CONTROL; SENSITIVITY ANALYSIS; SHEET METAL;

APPLICATION OF BAYESIAN NETWORKS; BAYESIAN APPROACHES; CONDITIONAL MUTUAL INFORMATION; CONDITIONAL PROBABILITY TABLES; DIAGNOSTIC PERFORMANCE; FIXTURE FAULT DIAGNOSIS; MANUFACTURING ENTERPRISE; MEASUREMENT DATA SETS;

BAYESIAN NETWORKS;

EID: 84880571369     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-012-4252-7     Document Type: Article

References (17)

1. Ceglarek D, Shi J (1996) Fixture failure diagnosis for auto body assembly using pattern recognition. J Manuf Sci Eng 118(1):55-66. doi: 10.1115/1.2803648
2. Rong Q, Ceglarek D, Shi J (2000) Dimensional fault diagnosis for compliant beam structure assemblies. J Manuf Sci Eng 122(3):773-780. doi: 10.1115/1.1285917
3. Liu YG, Hu SJ (2005) Assembly fixture fault diagnosis using designated component analysis. J Manuf Sci Eng 127(2):358-368. doi: 10.1115/1.1852572
4. Jin J, Shi J (1999) State space modeling of sheet metal assembly for dimensional control. J Manuf Sci Eng 121(4):756-762. doi: 10.1115/1.2833137
5. Tian Z, Lai X, Lin Z (2009) Robust fixture layout design for multi-station sheet metal assembly processes using a genetic algorithm. Int J Prod Res 47(21):6159-6176. doi: 10.1080/00207540802178091
6. Liu SC, Hu SJ (1997) Variation simulation for deformable sheet metal assemblies using finite element methods. J Manuf Sci Eng 119(3):368-374. doi: 10.1115/1.2831115
7. Camelio JA, Hu SJ, Yim H (2005) Sensor placement for effective diagnosis of multiple faults in fixturing of compliant parts. J Manuf Sci Eng 127(1):68-74. doi: 10.1115/1.1828056
8. Franciosa P, Gerbino S, Patalano S (2011) Simulation of variational compliant assemblies with shape errors based on morphing mesh approach. Int J Adv Manuf Technol 53(1-4):47-61. doi: 10.1007/s00170-010-2839-4
9. Niaki STA, Nezhad MSF (2009) Decision-making in detecting and diagnosing faults of multivariate statistical quality control systems. Int J Adv Manuf Technol 42(7-8):713-724. doi: 10.1007/s00170-008-1636-9
10. Rahim MA, Khalid HM, Akram M (2011) Quality monitoring of a closed-loop system with parametric uncertainties and external disturbances: a fault detection and isolation approach. Int J Adv Manuf Technol 55(1-4):293-306. doi: 10.1007/s00170-010-3043-2
11. Qin Y, Zhao Y, Yao Y, Xu D (2011) Multistage machining processes variation propagation analysis based on machining processes weighted network performance. Int J Adv Manuf Technol 55(5-8):487-499. doi: 10.1007/s00170-010- 3113-5
12. Wolbrecht E, Ambrosio BD, Paach B (2000) Monitoring and diagnosis of a multi-stage manufacturing process using Bayesian networks. AI EDAM 14(1):53-67. doi: 10.1017/S0890060400141058
13. Dey S, Stori JA (2005) A Bayesian network approach to root cause diagnosis of process variations. Int J Mach Tool Manu 45(1):75-91. doi: 10.1016/j.ijmachtools.2004.06018
14. Romessis C, Mathioudakis K (2006) Bayesian network approach for gas path fault diagnosis. J Eng Gas Turbines Power 128(1):64-72. doi: 10.1115/GT2004-53801
15. Liu Y, Jin S, Lin Z, Zheng C, Yu K (2011) Optimal sensor placement for fixture fault diagnosis using Bayesian network. Assem Autom 31(2):176-181. doi: 10.1108/01445151111117764
16. Heckerman D (2008) A tutorial on learning with Bayesian networks. Stud Comput Intel 156:33-82. doi: 10.1007/978-3-540-85066-3-3
17. Cheng J, Greiner R, Kelly J, Bell D, Liu W (2002) Learning Bayesian networks from data: an information-theory based approach. Artif Intell 137(1-2):43-90. doi: 10.1016/S0004-3702(02)00191-1