9.3.2 A Root Cause Analysis Method using Dual Vee Model for Cause Identification Reliability Improvement

2012 ◽  
Vol 22 (1) ◽  
pp. 1293-1307
Author(s):  
Hironori MAEJIMA ◽  
Naohiko KOHTAKE ◽  
Yoshiaki OHKAMI
Keyword(s):  
Root Cause Analysis ◽  
Analysis Method ◽  
Cause Analysis ◽  
Root Cause ◽  
Reliability Improvement

Sentiment Root Cause Analysis Based on Fuzzy Formal Concept Analysis and Fuzzy Cognitive Map

2016 ◽  
Vol 16 (3) ◽  
Author(s):  
Sang-Min Park ◽  
Young-Gab Kim ◽  
Doo-Kwon Baik
Keyword(s):  
Sentiment Analysis ◽  
Formal Concept Analysis ◽  
Root Cause Analysis ◽  
Fuzzy Cognitive Map ◽  
Formal Concept ◽  
Analysis Method ◽  
Cause Analysis ◽  
Root Cause ◽  
Fuzzy Formal Concept ◽  
Fuzzy Formal Concept Analysis

Feature-level sentiment analysis can retrieve the sentimental preferences for the features of products but cannot retrieve the causes of the preferences. Previous sentiment analysis methods used sentiment words to calculate the sentiment polarity for specific features but could not utilize neutral sentiment words, even when they constituted a large proportion of the sentiment words. Fault diagnosis can extract causes and determine the root cause by using factual information and the cause-effect relation, but is not used for sentiment data. For the retrieval of sentiment root causes, we propose a sentiment root cause analysis method for user preferences. We consider sentiment relations based on fuzzy formal concept analysis (FFCA) to extend hierarchical feature-level sentiment analysis. A hierarchical relation of neutral sentiment words and explicit causal relation based on causal conjunctions is utilized to retrieve the cross features of root causes. A sentiment root cause is determined from the extracted causes to explain the preference of a sentiment expression by using a fuzzy cognitive map with a relations method. We demonstrate a factual ontology and sentiment ontology based on a feature ontology for clothing products. We evaluated the proposed sentiment root cause analysis method and verified that it is improved as compared with term frequency-based methods and sentiment score analysis.

scholarly journals Risk Analysis in the Automotive Industry

2020 ◽  
Author(s):  
Annamária Koncz ◽  
László Pokorádi ◽  
Zsolt Csaba Johanyák
Keyword(s):  
Risk Analysis ◽  
Failure Mode ◽  
Automotive Industry ◽  
Root Cause Analysis ◽  
Automotive Sector ◽  
Analysis Method ◽  
Effect Analysis ◽  
Cause Analysis ◽  
Root Cause ◽  
Safety And Reliability

The automotive industry is one of the most dynamically growing fields of the manufacturingarea. Besides this, it has very strict rules concerning safety and reliability. In our work, our aim is to point out the importance of the automotive industry (based on statistics) and the rules in connection with risk and root cause analysis. The most important risk analysis method is the Failure Mode and Effect Analysis (FMEA). According to standards and OEM regulations, FMEA is obligatory in the automotive sector. In our study, we summarise the area of FMEA usage, its types and process steps.

scholarly journals Root Cause Analysis Using the Prevention and Recovery Information System for Monitoring and Analysis Method in Healthcare Facilities

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Babiche E.J.M. Driesen ◽  
Mees Baartmans ◽  
Hanneke Merten ◽  
René Otten ◽  
Camilla Walker ◽  
...  
Keyword(s):  
Information System ◽  
Root Cause Analysis ◽  
Analysis Method ◽  
Cause Analysis ◽  
Healthcare Facilities ◽  
Root Cause

Improvement of Shape Accuracy in Press-Formed Parts of High-Strength Steel by Springback-Root-Cause Analysis

2016 ◽  
Vol 725 ◽  
pp. 610-615
Author(s):  
Jiro Hiramoto ◽  
Masaki Urabe ◽  
Akinobu Ishiwatari ◽  
Fusahito Yoshida
Keyword(s):  
High Strength Steel ◽  
High Strength ◽  
Root Cause Analysis ◽  
Body Parts ◽  
Analysis Method ◽  
Shape Accuracy ◽  
Cause Analysis ◽  
Automotive Body ◽  
Root Cause ◽  
Actual Part

Springback is one of the most serious problems in high-strength steel-sheet forming to produce automotive body parts. A springback-root-cause analysis method was developed to identify the areas of stresses at the bottom dead point, which are the most influential in springback. The analysis method of using springback driving stresses, that is, the difference between stresses before and after springback, is more accurate to eliminate the effect of the residual stresses on springback. This analysis method was applied to both a simple model and the forming of an actual part to verify this analysis method. The areas of stresses that have a major impact on springback were identified. A countermeasure against the actual part springback based on this result was devised and it was clarified that the countermeasure is effective on the springback.

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