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.

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This paper presents XGBoost classifier-based methods to solve three tasks proposed by the European Prognostics and Health Management Society (PHME) 2022 conference. These tasks are based on real data from a Surface Mount Technologies line. Each of these tasks aims to improve the efficiency of the Printed Circuit Board (PCB) manufacturing process, facilitate the operator’s work and minimize the cases of manual intervention. Due to the structured nature of the problems proposed for each task, an XGBoost method based on encoding and feature engineering is proposed. The proposed methods utilise the fusion of test values and system characteristics extracted from two different testing equipment of the Surface Mount Technologies lines. This work also explores the problems of generalising prediction at the system level using information from the subsystem data. For this particular industrial case: the challenges with the changes in the number of subsystems. For Industry 4.0, the need for interpretability is very important. This is why the results of the models are analysed using Shapley values. With the proposed method, our team took the first place, capable of successfully detecting at an early stage the defective components for tasks 2 and 3.