Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 12/2021

10.11.2020 | Original Article

Product failure prediction with missing data using graph neural networks

verfasst von: Seokho Kang

Erschienen in: Neural Computing and Applications | Ausgabe 12/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In real-world production data, missing values often occur randomly or systematically with various missing patterns. Missing values need to be handled properly to build effective prediction models. This paper presents a novel method based on graph representation and graph neural networks for improving prediction in missing value conditions. To utilize the entire information of a training dataset without direct manipulation, all instances of the dataset are represented as graphs of varying sizes, in which nodes and edges represent the observed input variables and their pairwise relationships. Prediction models learn from the graph representations. These models can make predictions of unknown labels for new instances that have arbitrary missing patterns. The superiority of the proposed method was investigated on seven different product failure prediction tasks from a home appliance manufacturer. The proposed method outperformed all other methods in six of the seven tasks.
Vorheriger Artikel The detection, tracking, and temporal action localisation of swimmers for automated analysis
Nächster Artikel An efficient and secure feature location approach in source code using Jacobian matrix-based clustering

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, et al (2018) Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:​180601261
2.
Camino RD, Hammerschmidt CA, State R (2019) Improving missing data imputation with deep generative models. arXiv preprint arXiv:​190210666
3.
Chien CF, Wang WC, Cheng JC (2007) Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Syst Appl 33(1):192–198CrossRef
4.
Chien CF, Liu CW, Chuang SC (2017) Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement. Int J Prod Res 55(17):5095–5107CrossRef
5.
Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of conference on empirical methods in natural language processing, pp 1724–1734
6.
Choudhary AK, Harding JA, Tiwari MK (2009) Data mining in manufacturing: a review based on the kind of knowledge. J Intel Manuf 20(5):501–521CrossRef
7.
Choudhury SJ, Pal NR (2019) Imputation of missing data with neural networks for classification. Knowl Based Syst 182:104838CrossRef
8.
García-Laencina PJ, Sancho-Gómez JL, Figueiras-Vidal AR (2010) Pattern classification with missing data: a review. Neural Comput Appl 19(2):263–282CrossRef
9.
Ghorai S, Mukherjee A, Gangadaran M, Dutta PK (2013) Automatic defect detection on hot-rolled flat steel products. IEEE Trans Instrum Meas 62(3):612–621CrossRef
10.
Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: Proceedings of international conference on machine learning, pp 1263–1272
11.
He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284CrossRef
12.
He W, Li Z, Chen CLP (2017) A survey of human-centered intelligent robots: issues and challenges. IEEE/CAA J Autom Sin 4(4):602–609CrossRef
13.
Hong TP, Wu CW (2011) Mining rules from an incomplete dataset with a high missing rate. Expert Syst Appl 38(4):3931–3936CrossRef
14.
Jia X, Di Y, Feng J, Yang Q, Dai H, Lee J (2018) Adaptive virtual metrology for semiconductor chemical mechanical planarization process using gmdh-type polynomial neural networks. J Process Control 62:44–54CrossRef
15.
Kang S (2020) Joint modeling of classification and regression for improving faulty wafer detection in semiconductor manufacturing. J Intel Manuf 31:319–326CrossRef
16.
Kang S, Kim E, Shim J, Cho S, Chang W, Kim J (2017) Mining the relationship between production and customer service data for failure analysis of industrial products. Comput Ind Eng 106:137–146CrossRef
17.
18.
Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of international conference on learning representations
19.
Köksal G, Batmaz I, Testik MC (2011) A review of data mining applications for quality improvement in manufacturing industry. Expert Syst Appl 38(10):13448–13467CrossRef
20.
Kumar S, Chow TWS, Pecht M (2010) Approach to fault identification for electronic products using mahalanobis distance. IEEE Trans Instrum Meas 59(8):2055–2064CrossRef
21.
Kwak DS, Kim KJ (2012) A data mining approach considering missing values for the optimization of semiconductor-manufacturing processes. Expert Syst Appl 39(3):2590–2596CrossRef
22.
Lakshminarayan K, Harp SA, Samad T (1999) Imputation of missing data in industrial databases. Appl Intel 11(3):259–275CrossRef
23.
Li Y, Tarlow D, Brockschmidt M, Zemel R (2016) Gated graph sequence neural networks. In: Proceedings of international conference on learning representations
24.
Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley, New JerseyCrossRef
25.
Myneni MB, Srividya Y, Dandamudi A (2017) Correlated cluster-based imputation for treatment of missing values. In: Proceedings of international conference on computational intelligence and informatics, pp 171–178
26.
Qin SJ (2014) Process data analytics in the era of big data. AICHE J 60(9):3092–3100CrossRef
27.
Rodriguez JD, Perez A, Lozano JA (2010) Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans Pattern Anal Mach Intel 32(3):569–575CrossRef
28.
Shin CK, Park SC (2000) A machine learning approach to yield management in semiconductor manufacturing. Int J Prod Res 38(17):4261–4271CrossRef
29.
Siddiqui YA, Saif AWA, Cheded L, Elshafei M, Rahim A (2015) Integration of multivariate statistical process control and engineering process control: a novel framework. Int J Adv Manuf Technol 78(1–4):259–268CrossRef
30.
Silva LO, Zárate LE (2014) A brief review of the main approaches for treatment of missing data. Intel Data Anal 18(6):1177–1198CrossRef
31.
32.
33.
Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. In: Proceedings of international conference on learning representations
34.
Wang Z, Liu L, Zhang H (2017) Neural network-based model-free adaptive fault-tolerant control for discrete-time nonlinear systems with sensor fault. IEEE Trans Syst Man Cybern Syst 47(8):2351–2362CrossRef
35.
Wuest T, Weimer D, Irgens C, Thoben KD (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4(1):23–45
36.
Yang CC (2008) Improving the definition and quantification of quality costs. Total Qual Manag 19(3):175–191CrossRef
37.
Yang HC, Tieng H, Cheng FT (2016) Automatic virtual metrology for wheel machining automation. Int J Prod Res 54(21):6367–6377CrossRef
38.
Yin S, Ding SX, Xie X, Luo H (2014) A review on basic data-driven approaches for industrial process monitoring. IEEE Trans Ind Electr 61(11):6418–6428CrossRef
39.
Yoon J, Jordon J, Schaar M (2018) GAIN: Missing data imputation using generative adversarial nets. In: Proceedings of international conference on machine learning, pp 5675–5684
40.
Zhou J, Cui G, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2018) Graph neural networks: a review of methods and applications. arXiv preprint arXiv:​181208434
41.
Zhu X, Zhang S, Jin Z, Zhang Z, Xu Z (2011) Missing value estimation for mixed-attribute data sets. IEEE Trans Knowl Data Eng 23(1):110–121CrossRef
Metadaten
Titel
Product failure prediction with missing data using graph neural networks
verfasst von
Seokho Kang
Publikationsdatum
10.11.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05486-2

Weitere Artikel der Ausgabe 12/2021

Neural Computing and Applications 12/2021 Zur Ausgabe

Original Article

Robust and high-order correlation alignment for unsupervised domain adaptation

Original Article

Flexible data representation with graph convolution for semi-supervised learning

Original Article

Neural-assisted image-dependent encryption scheme for medical image cloud storage

Original Article

DL-SCALE: a novel deep learning-based model order upscaling scheme for solving topology optimization problems

Original Article

Improved coral reefs optimization with adaptive -hill climbing for feature selection

Original Article

Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles

Premium Partner

    Bildnachweise