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

doi:10.1007/s12204-016-1708-1

Abstract

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

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

CLC Number:

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]. Journal of shanghai Jiaotong University (Science), 2016, 21(2): 164-172.

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