Prediction and Diagnosis of Propagated Errors in Assembly Systems Using Virtual Factories

Baydar, Cem M.

doi:10.1115/1.1411966

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Faults can also be abrupt (with immediate effects) or subtle (causing slow system degradation). Particularly difficult task is diagnosing multiple and interactive faults, especially in the cases of error propagation ( [9]). …”

Section: A Model-based Diagnosis Of Hybrid Systemsmentioning

confidence: 99%

Using behavior models for anomaly detection in hybrid systems

Vodenčarević

Büning

Niggemann

et al. 2011

2011 XXIII International Symposium on Information, Communication and Automation Technologies

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The importance of safety and reliability in today's real-world complex hybrid systems, such as process plants, led to the development of various anomaly detection and diagnosis techniques. Model-based approaches established themselves among the most successful ones in the field. However, they depend on a model of a system, which usually needs to be derived manually. Manual modeling requires a lot of efforts and resources.This paper gives a procedure for anomaly detection in hybrid systems that uses automatically generated behavior models. The model is learned from logged system's measurements in a hybrid automaton framework. The presented anomaly detection algorithm utilizes the model to predict the system behavior, and to compare it with the observed behavior in an online manner. Alarms are raised whenever a discrepancy is found between these two. The effectiveness of this approach is demonstrated in detecting several types of anomalies in a real-world running production system.

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Section: A Model-based Diagnosis Of Hybrid Systemsmentioning

confidence: 99%

Using behavior models for anomaly detection in hybrid systems

Vodenčarević

Büning

Niggemann

et al. 2011

2011 XXIII International Symposium on Information, Communication and Automation Technologies

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“…The threshold level of the belief value for automated recovery was taken as 0.8. During this simulation process several types of error-propagation were observed (Baydar and Saitou, 2001c). One example is discussed below.…”

Section: Multi-station Assembly Systemmentioning

confidence: 99%

“…Having the sensory symptoms and their associated failure type and 3-D-state, these conditions are stored and used for the diagnosis using Bayesian Reasoning. Next step is using an off-line error recovery system to generate robust recovery plans (Baydar and Saitou, 2001c) that can deal with multiple error conditions of similar nature using Genetic Programming (Baydar and Saitou, 2001b;Koza, 1992). Finally, this of¯ine recovery system can be downloaded to the controller of the robotic system to patch the process.…”

Section: Introductionmentioning

confidence: 99%

Off-line error prediction, diagnosis and recovery using virtual assembly systems

Baydar

2004

Journal of Intelligent Manufacturing

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Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of many parameters, it is dif®cult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focussing on on-line diagnosing and recovery of the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the possible errors and they are de®cient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the 3-D model of the assembly line to predict the possible errors in an off-line manner. After that, these predicted errors are diagnosed and recovered using Bayesian reasoning and genetic algorithms. Several case studies are performed on single-station and multi-station assembly systems and the results are discussed. It is expected that with this new approach, errors can be diagnosed and recovered accurately and costly downtimes of robotic assembly systems will be reduced.

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Further potentials of CAQ tools for fault handling in automotive manufacturing processes

Reinhart

Rashidy

2008

Prod. Eng. Res. Devel.

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The example of automotive body-in-white production is used to illustrate further potentials for automated fault handling in operative quality control. A review of pertaining literature on process modeling and control suggests some trends and future practices in research and industry. The paper discusses current results of a research project aimed at devising novel process and knowledge modeling concepts for online fault diagnosis and recovery. The proposed approach differentiates between three different tasks: fault recognition, fault identification, and decision and feedback. The paper thus presents a possible basis for future automation of fault analysis and recovery tasks in a variety of manufacturing environments.

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Prediction and Diagnosis of Propagated Errors in Assembly Systems Using Virtual Factories

Cited by 5 publications

References 10 publications

Using behavior models for anomaly detection in hybrid systems

Using behavior models for anomaly detection in hybrid systems

Off-line error prediction, diagnosis and recovery using virtual assembly systems

Further potentials of CAQ tools for fault handling in automotive manufacturing processes

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