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Publications

2023

Overlap in Automatic Root Cause Analysis in Manufacturing: An Information Theory-Based Approach

Authors
Oliveira, EE; Migueis, VL; Borges, JL;

Publication
APPLIED SCIENCES-BASEL

Abstract
Automatic Root Cause Analysis solutions aid analysts in finding problems' root causes by using automatic data analysis. When trying to locate the root cause of a problem in a manufacturing process, an issue-denominated overlap can occur. Overlap can impede automated diagnosis using algorithms, as the data make it impossible to discern the influence of each machine on the quality of products. This paper proposes a new measure of overlap based on an information theory concept called Positive Mutual Information. This new measure allows for a more detailed analysis. A new approach is developed for automatically finding the root causes of problems when overlap occurs. A visualization that depicts overlapped locations is also proposed to ease practitioners' analysis. The proposed solution is validated in simulated and real case-study data. Compared to previous solutions, the proposed approach improves the capacity to pinpoint a problem's root causes.

2023

Automatic root cause analysis in manufacturing: an overview & conceptualization

Authors
Oliveira, EE; Migueis, VL; Borges, JL;

Publication
JOURNAL OF INTELLIGENT MANUFACTURING

Abstract
Root cause analysis (RCA) is the process through which we find the true cause of a problem. It is a crucial process in manufacturing, as only after finding the root cause and addressing it, it is possible to improve the manufacturing operation. However, this is a very time-consuming process, especially if the amount of data about the manufacturing operation is considerable. With the increase in automation and the advent of Industry 4.0, sensorization of manufacturing environments has expanded, increasing with it the data available. The conjuncture described gives rise to the challenge and the opportunity of automatizing root cause analysis (at least partially), making this process more efficient, using tools from data mining and machine learning to help the analyst find the root cause of a problem. This paper presents an overview of the literature that has been published in the last 17 years on developing automatic root cause analysis (ARCA) solutions in manufacturing. The literature on the topic is disperse and it is currently lacking a connecting thread. As such, this study analyzes how previous studies developed the different elements of an ARCA solution for manufacturing: the types of data used, the methodologies, and the evaluation measures of the methods proposed. The proposed conceptualization establishes the base on which future studies on ARCA can develop results from this analysis, identifying gaps in the literature and future research opportunities.

2022

Understanding Overlap in Automatic Root Cause Analysis in Manufacturing Using Causal Inference

Authors
Oliveira, EE; Migueis, VL; Borges, JL;

Publication
IEEE ACCESS

Abstract
Overlap has been identified in previous works as a significant obstacle to automated diagnosis using data mining algorithms, since it makes it impossible to discern how each machine influences product quality. Several solutions that handle overlap have been proposed, but the final result is a list of potential overlapped root causes. The goal of this paper is to develop a solution resilient to overlap that can determine the true root cause from a list of possible root causes, when possible, and determine the conditions in which it is possible to identify the root causes. This allows for a better understanding of overlap, and enables the development of a fully automatic root cause analysis for manufacturing. To do so, we propose an automatic root cause analysis approach that uses causal inference and do calculus to determine the true root cause. The proposed approach was validated on simulated and real case-study data, and allowed for an estimation of the effect of a product passing through a certain machine while disregarding the effect of overlap, in certain conditions. The results were on par with the state-of-the-art solutions capable of handling overlap. The contributions of this paper are a graphical definition of overlap, the identification of the conditions in which is possible to overcome the effect of overlap, and a solution that can present a single true root cause when such conditions are met.

2022

On the influence of overlap in automatic root cause analysis in manufacturing

Authors
Oliveira, EE; Migueis, VL; Borges, JL;

Publication
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Abstract
To improve manufacturing processes, it is essential to find the root causes of occurring problems, in order to solve them permanently. Automatic Root Cause Analysis (ARCA) solutions aid analysts in finding such root causes, by using automatic data analysis to improve the digital decision. When trying to locate the root cause of a problem in a manufacturing process, a phenomenon can occur that disrupts the application of ARCA solutions. Overlap, as we denominated, is a phenomenon where local synchronicities in the manufacturing process lead to data where it is impossible to discern the influence of each location in the quality of products, which impedes automated diagnosis, especially when using classifiers. This paper identifies and defines overlap, and proposes a two-phase ARCA solution that uses factor-ranking algorithms, instead of classifiers. The proposed solution is evaluated in simulated and real case-study data. Results proved the presence of overlap in the datasets, and its negative impact on classifiers. The proposed solution has a positive performance detecting root causes even in the presence of overlap.

2022

VisAC: An interactive tool for visual analysis of consanguinity in the ancestry of individuals

Authors
Borges, J;

Publication
INFORMATION VISUALIZATION

Abstract
Genealogy studies are growing in popularity, and researchers are increasingly using visualization methods to summarize and communicate their findings. A family tree is a visual representation of family members and their relationships that is commonly used to support the research of a family's history and publish the results. In some cases, an ancestor may occur in more than one place in the lineage of an individual, which is one of the reasons for the occurrence of consanguineous marriages, that is, marriages between blood relative spouses. Current methods for family tree visualization were not designed to analyze and assess the level of consanguinity in the ancestry of individuals. This paper proposes VisAC, an interactive tool to support the visual analysis of consanguinity in individuals' ancestry. The inbreeding coefficient is used as a measure of consanguinity. The coefficient corresponds to an estimate of the probability that two alleles (a variant of a given gene) in the DNA were inherited from the same individual. A visualization design and an interactive tool were developed with genealogists' support. In addition, the feedback collected through a questionnaire about two demo videos and tests with three target users strongly supports the effectiveness of the family tree visual representation and the adequacy of the interactive tool for the exploratory analysis task. Real-world examples are given to illustrate the usefulness of the visualization design, and an example of exploratory analysis is presented to illustrate the use of the interactive tool. In summary, this work formulates the task of visual analysis of consanguinity in ancestors' trees and proposes VisAC, a new visualization tool to support the task.

Supervised
thesis

2022

Towards fully autonomous detection of contextual fabric anomalies through supervised deep learning models

Author
Diogo Costa Cunha

Institution
UP-FEUP

2022

Previsão do Tempo de Venda de um Imóvel Recorrendo a Data Analytics

Author
Nadine Santos Carvalho

Institution
UP-FEUP

2022

Previsão de Vendas de um Marketplace Digital através de Técnicas de Machine Learning

Author
Ricardo Moura Vieira Batista

Institution
UP-FEUP

2022

Prediction of Shell Finite-Element Stresses using Convolutional Neural Networks (CNN)

Author
Nuno Gonçalo Dias Gaspar

Institution
UP-FEUP

2021

Overlap in Automatic Root Cause Analysis in Manufacturing

Author
Eduardo Luís de Meireles e Oliveira

Institution
UP-FEUP