Anwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontage

2024-07-23 · Lucas Possner, Lukas Bahr, Leonard Roehl, Christoph Wehner, Sophie Groeger

Root Cause Analysis (RCA) is a quality management method that aims to systematically investigate and identify the cause-and-effect relationships of problems and their underlying causes. Traditional methods are based on the analysis of problems by subject matter experts. In modern production processes, large amounts of data are collected. For this reason, increasingly computer-aided and data-driven methods are used for RCA. One of these methods are Causal Discovery Algorithms (CDA). This publication demonstrates the application of CDA on data from the assembly of a leading automotive manufacturer. The algorithms used learn the causal structure between the characteristics of the manufactured vehicles, the ergonomics and the temporal scope of the involved assembly processes, and quality-relevant product features based on representative data. This publication compares various CDAs in terms of their suitability in the context of quality management. For this purpose, the causal structures learned by the algorithms as well as their runtime are compared. This publication provides a contribution to quality management and demonstrates how CDAs can be used for RCA in assembly processes.

📄 PDF Abstract BibTeX arXiv:2407.16388

Code (0)

등록된 구현이 없습니다.

Tasks

Causal DiscoveryManagement

Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization

2023-02-03 · Dongjie Wang, Zhengzhang Chen, Jingchao Ni, Liang Tong 외

In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal relationships for root cause localization.…

Causal Discovery Time Series Time Series Analysis

Root cause analysis via difference graph discovery from linear time-series data

2026-08-21 · Anouk Ruer, Timothée Loranchet, Daria Bystrova, Charles K. Assaad arxiv

Root cause analysis aims to identify the mechanisms responsible for anomalies in complex dynamical systems. In this paper, we study root cause analysis in linear time-series through the lens of difference graph discovery…

Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning

2024-08-11 · Shi Bo, Minheng Xiao

This paper presents a novel approach to root cause attribution of delivery risks within supply chains by integrating causal discovery with reinforcement learning. As supply chains become increasingly complex, traditional…

Causal Discovery Decision Making reinforcement-learning Reinforcement Learning

Root Cause Analysis In Microservice Using Neural Granger Causal Discovery

2024-02-02 · Cheng-Ming Lin, Ching Chang, Wei-Yao Wang, Kuang-Da Wang 외

In recent years, microservices have gained widespread adoption in IT operations due to their scalability, maintenance, and flexibility. However, it becomes challenging for site reliability engineers (SREs) to pinpoint th…

Causal Discovery Contrastive Learning CPU Time Series+1

Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining

2026-05-26 · Biao Ouyang, Tengxue Zhang, Zhihao Zhuang, Yang Shu 외 arxiv

Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies. Existing approaches typically rely on dataset-specific optimization, making it difficult to tr…