Fault diagnosis for automotive assembly based on optical coordinate data and machine learning

Xuan Zeng

doi:10.1117/12.2505443

31 August 2018 Fault diagnosis for automotive assembly based on optical coordinate data and machine learning

Xuan Zeng

Proceedings Volume 10835, Global Intelligence Industry Conference (GIIC 2018); 1083509 (2018) https://doi.org/10.1117/12.2505443
Event: Global Intelligent Industry Conference 2018, 2018, Beijing, China

Abstract

For fault diagnosis of automotive assembly, the experience knowledge of experts is still the primary means which is inconvenient and time-consuming. This paper proposes a novel fault diagnosis method for automotive assembly based on optical coordinate data and machine learning. First, after obtaining large amounts of measured data from sensors, a feature selection method which is based on the subset-level score is performed. Because high-dimensional data will result in high computational cost and irrelevant and redundant features may also degrade the performance of fault diagnosis. The feature selection method which can be classified into filter methods needs to choose a certain evaluation criterion first. By using a fast iterative algorithm, we can finally find the optimal subset of features. Second, for the nonlinear relationship between measured data, we introduced well-known kernel methods to efficiently improve feature extraction effect. In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known is the support vector machines. Last, Bayesian inference classifier is built to recognize the fault pattern. The experimental on real production process data of automotive assembly show that the proposed method can greatly enhance the diagnosis accuracy.

Citation Download Citation

Xuan Zeng "Fault diagnosis for automotive assembly based on optical coordinate data and machine learning", Proc. SPIE 10835, Global Intelligence Industry Conference (GIIC 2018), 1083509 (31 August 2018); https://doi.org/10.1117/12.2505443

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KEYWORDS

Feature selection

Bayesian inference

Feature extraction

Machine learning

Data acquisition

Dimension reduction

Data processing

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