Testing equipments are a crucial part of production quality control in the automotive industry. Their health needs to be controlled carefully to avoid quality issues and false alarms that reduce production efficiency, potentially leading to huge losses. The main challenge for this control is the large number of features leaning for automated reasoning. A data-based Health Monitoring System could be a solution.
In manufacturing industries, a widely accepted index for evaluating process performance is the capability. It combines statistical measures for normal distributions in order to verify the ability of a process to produce an output within the specification limits. In this article we propose a capability-based prognosis and diagnosis method based on test data. Capability is calculated and compared to a known threshold. If the index value exceeds the threshold, then a diagnosis phase is initiated to find out which parts of the equipment are faulty. Data temporality is also taken into account. Data trends are used for prognosis.Test data are splited into periods. To respect the normality assumption of the capability, it is proposed to use a Gaussian Mixture Model (GMM) classification to extract all normal distributions found in one data period.
Two approaches are discussed for selecting the number of clusters used for the classification. The first approach is based on the well-known Bayesian Information Criterion (BIC). The second approach uses a multi-criteria aggregation function learned by using machine learning on a synthetically gene-rated dataset. Some of the criteria used in the aggregation are inference based. Others are classical statistics extracted from the classes obtained by the GMM.For each of these classes the capability index is calculated and used for diagnosis and prognosis purposes.
This method is applied on real data from In-Circuit Testing (ICT) machines for electronic components at a Vitesco factory in France.
How to Cite
Process capability, Gaussian Mixture Model, Diagnosis, Prognosis, Model selection, Automotive manufacturing, Machine learning, Data-based health monitoring
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.