Artificial Intelligence for Quality Assurance and Troubleshooting in Industry

Abstract

This paper presents the new X-Quality conceptional framework, that applies Artificial Intelligence (AI) to contribute to the improvement of quality assurance and troubleshooting in manufacturing. The goal is to identify and resolve quality issues effectively using AI techniques, applying Explainable AI (XAI) and stream reasoning to ensure transparency and comprehensibility to find causes for predicted quality defects. There are mainly three approaches of the framework described, that are tackling typical industry challenges. The first approach combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for time series quality prediction with SHApley Additive exPlanations (SHAP) to explain the LSTM-CNN. The second method combines Machine Learning (ML) and Fault Tree Analysis (FTA) methods for comprehensive fault detection and analysis. The third technique applies semantic reasoning for real-time contextualization and root cause identification.