A Bayesian Based Process Monitoring and Fixture Fault Diagnosis Approach in the Auto Body Assembly Process

doi:10.1007/s12204-016-1708-1

上海交通大学学报（英文版） ›› 2016, Vol. 21 ›› Issue (2): 164-172.doi: 10.1007/s12204-016-1708-1

A Bayesian Based Process Monitoring and Fixture Fault Diagnosis Approach in the Auto Body Assembly Process

LIU Yinhua1* (刘银华), YE Xialiang1 (叶夏亮), JIN Sun2 (金隼)

(1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China)

出版日期:2016-04-15 发布日期:2016-04-26
通讯作者: LIU Yinhua (刘银华) E-mail:liuyinhua@usst.edu.cn

A Bayesian Based Process Monitoring and Fixture Fault Diagnosis Approach in the Auto Body Assembly Process

LIU Yinhua1* (刘银华), YE Xialiang1 (叶夏亮), JIN Sun2 (金隼)

(1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China)

Online:2016-04-15 Published:2016-04-26
Contact: LIU Yinhua (刘银华) E-mail:liuyinhua@usst.edu.cn

摘要/Abstract

摘要： The auto body process monitoring and the root cause diagnosis based on data-driven approaches are vital ways to improve the dimension quality of sheet metal assemblies. However, during the launch time of the process mass production with an off-line measurement strategy, the traditional statistical methods are difficult to perform process control effectively. Based on the powerful abilities in information fusion, a systematic Bayesian based quality control approach is presented to solve the quality problems in condition of incomplete dataset. For the process monitoring, a Bayesian estimation method is used to give out-of-control signals in the process. With the abnormal evidence, the Bayesian network (BN) approach is employed to identify the fixture root causes. A novel BN structure and the conditional probability training methods based on process knowledge representation are proposed to obtain the diagnostic model. Furthermore, based on the diagnostic performance analysis, a case study is used to evaluate the effectiveness of the proposed approach. Results show that the Bayesian based method has a better diagnostic performance for multi-fault cases.

关键词: dimension quality, Bayesian method, process knowledge, fault diagnosis

Abstract: The auto body process monitoring and the root cause diagnosis based on data-driven approaches are vital ways to improve the dimension quality of sheet metal assemblies. However, during the launch time of the process mass production with an off-line measurement strategy, the traditional statistical methods are difficult to perform process control effectively. Based on the powerful abilities in information fusion, a systematic Bayesian based quality control approach is presented to solve the quality problems in condition of incomplete dataset. For the process monitoring, a Bayesian estimation method is used to give out-of-control signals in the process. With the abnormal evidence, the Bayesian network (BN) approach is employed to identify the fixture root causes. A novel BN structure and the conditional probability training methods based on process knowledge representation are proposed to obtain the diagnostic model. Furthermore, based on the diagnostic performance analysis, a case study is used to evaluate the effectiveness of the proposed approach. Results show that the Bayesian based method has a better diagnostic performance for multi-fault cases.

Key words: dimension quality, Bayesian method, process knowledge, fault diagnosis

中图分类号:

TH 161

LIU Yinhua1* (刘银华), YE Xialiang1 (叶夏亮), JIN Sun2 (金隼). A Bayesian Based Process Monitoring and Fixture Fault Diagnosis Approach in the Auto Body Assembly Process[J]. 上海交通大学学报（英文版）, 2016, 21(2): 164-172.

LIU Yinhua1* (刘银华), YE Xialiang1 (叶夏亮), JIN Sun2 (金隼). A Bayesian Based Process Monitoring and Fixture Fault Diagnosis Approach in the Auto Body Assembly Process[J]. Journal of shanghai Jiaotong University (Science), 2016, 21(2): 164-172.

参考文献 12

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[8]	DING Y, CEGLAREK D, SHI J. Fault diagnosis of multistage manufacturing processes by using state space approach [J]. Journal of Manufacturing Science and Engineering, 2002, 124(2): 313-322.
[9]	ZHOU S, CHEN Y, SHI J. Statistical estimation and testing for variation root-cause determination of multistage manufacturing processes [J]. IEEE Transactions on Automation Science and Engineering, 2004, 1(1):73-83.
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A Bayesian Based Process Monitoring and Fixture Fault Diagnosis Approach in the Auto Body Assembly Process

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