A big data-driven root cause analysis system: Application of Machine Learning in quality problem solving

2021 ◽  
pp. 107580
Author(s):  
Qiuping Ma ◽  
Hongyan Li ◽  
Anders Thorstenson
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Problem Solving ◽  
System Application ◽  
Root Cause Analysis ◽  
Data Driven ◽  
Cause Analysis ◽  
Quality Problem ◽  
Root Cause ◽  
Analysis System

LADRA: Log-based abnormal task detection and root-cause analysis in big data processing with Spark

2019 ◽  
Vol 95 ◽  
pp. 392-403 ◽  
Author(s):  
Siyang Lu ◽  
Xiang Wei ◽  
Bingbing Rao ◽  
Byungchul Tak ◽  
Long Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Root Cause Analysis ◽  
Big Data Processing ◽  
Cause Analysis ◽  
Root Cause

Datengetriebene Steigerung der Verfügbarkeit/Data-supported root cause analysis to increase the availability of machine tools. Data-driven availability increase

2020 ◽  
Vol 110 (07-08) ◽  
pp. 532-535
Author(s):  
Eckhart Uhlmann ◽  
Roman Dumitrescu ◽  
Julian Polte ◽  
Maurice Meyer ◽  
Deniz Simsek
Keyword(s):  
Data Analysis ◽  
Machine Tools ◽  
Critical Factor ◽  
Root Cause Analysis ◽  
Data Driven ◽  
Manufacturing Companies ◽  
Cause Analysis ◽  
Root Cause ◽  
Comprehensive Data ◽  
Causes Of Failure

Die Zuverlässigkeit von Werkzeugmaschinen ist ein kritischer Faktor für den Erfolg produzierender Unternehmen. Durch die Analyse von Daten in der Produktplanung können Maschinenhersteller Ausfallursachen eliminieren und Maschinen systematisch verbessern. Jedoch stellt eine umfassende Datenanalyse viele Unternehmen vor große Herausforderungen. Die in diesem Beitrag vorgestellte Methodik adressiert diese Problematik und unterstützt Unternehmen bei der zielgerichteten Datenanalyse.   The reliability of machine tools is a critical factor for the success of manufacturing companies. By analyzing data in product planning, machine manufacturers can eliminate causes of failure and systematically improve machines. However, comprehensive data analysis poses great challenges for many companies. The methodology presented in this paper addresses this problem and supports companies in the goal-driven data analysis.

scholarly journals Root cause analysis of failures and quality deviations in manufacturing using machine learning

Procedia CIRP ◽  
2018 ◽  
Vol 72 ◽  
pp. 1057-1062 ◽  
Author(s):  
Anna Lokrantz ◽  
Emil Gustavsson ◽  
Mats Jirstrand
Keyword(s):  
Machine Learning ◽  
Root Cause Analysis ◽  
Cause Analysis ◽  
Root Cause ◽  
Analysis Of Failures

scholarly journals Lifelong Machine Learning and root cause analysis for large-scale cancer patient data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Gautam Pal ◽  
Xianbin Hong ◽  
Zhuo Wang ◽  
Hongyi Wu ◽  
Gangmin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lifelong Learning ◽  
Root Cause Analysis ◽  
Cause Analysis ◽  
Training Time ◽  
Root Cause ◽  
Random Decision Forest ◽  
Batch Data ◽  
Over Time ◽  
Decision Forest

Abstract Introduction This paper presents a lifelong learning framework which constantly adapts with changing data patterns over time through incremental learning approach. In many big data systems, iterative re-training high dimensional data from scratch is computationally infeasible since constant data stream ingestion on top of a historical data pool increases the training time exponentially. Therefore, the need arises on how to retain past learning and fast update the model incrementally based on the new data. Also, the current machine learning approaches do the model prediction without providing a comprehensive root cause analysis. To resolve these limitations, our framework lays foundations on an ensemble process between stream data with historical batch data for an incremental lifelong learning (LML) model. Case description A cancer patient’s pathological tests like blood, DNA, urine or tissue analysis provide a unique signature based on the DNA combinations. Our analysis allows personalized and targeted medications and achieves a therapeutic response. Model is evaluated through data from The National Cancer Institute’s Genomic Data Commons unified data repository. The aim is to prescribe personalized medicine based on the thousands of genotype and phenotype parameters for each patient. Discussion and evaluation The model uses a dimension reduction method to reduce training time at an online sliding window setting. We identify the Gleason score as a determining factor for cancer possibility and substantiate our claim through Lilliefors and Kolmogorov–Smirnov test. We present clustering and Random Decision Forest results. The model’s prediction accuracy is compared with standard machine learning algorithms for numeric and categorical fields. Conclusion We propose an ensemble framework of stream and batch data for incremental lifelong learning. The framework successively applies first streaming clustering technique and then Random Decision Forest Regressor/Classifier to isolate anomalous patient data and provides reasoning through root cause analysis by feature correlations with an aim to improve the overall survival rate. While the stream clustering technique creates groups of patient profiles, RDF further drills down into each group for comparison and reasoning for useful actionable insights. The proposed MALA architecture retains the past learned knowledge and transfer to future learning and iteratively becomes more knowledgeable over time.

scholarly journals FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

2021 ◽  
Vol 116 ◽  
pp. 30-48
Author(s):  
Bram Steenwinckel ◽  
Dieter De Paepe ◽  
Sander Vanden Hautte ◽  
Pieter Heyvaert ◽  
Mohamed Bentefrit ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Data Streams ◽  
Expert Knowledge ◽  
Root Cause Analysis ◽  
Sensor Data ◽  
Cause Analysis ◽  
Root Cause

scholarly journals An evidence-based toolkit for the development of effective and sustainable root cause analysis system safety solutions

2013 ◽  
Vol 33 (2) ◽  
pp. 11-20 ◽  
Author(s):  
A. Zachary Hettinger ◽  
Rollin J. Fairbanks ◽  
Sudeep Hegde ◽  
Alexandra S. Rackoff ◽  
John Wreathall ◽  
...  
Keyword(s):  
Root Cause Analysis ◽  
Evidence Based ◽  
System Safety ◽  
Cause Analysis ◽  
Root Cause ◽  
Analysis System
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