A big data MapReduce framework for fault diagnosis in cloud-based manufacturing

International Journal of Production Research

Volumn 54, Issue 23, 2016, Pages 7060-7073

  Kumar, Ajay ^a   Shankar, Ravi ^a   Choudhary, Alok ^b   Thakur, Lakshman S ^c  

^a INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

^b LOUGHBOROUGH UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

big data analytics; class imbalance problem; fault diagnosis and Cloud based manufacturing; radial basis function; support vector machine (SVM)

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIG DATA; CLASSIFICATION (OF INFORMATION); DATA HANDLING; FAILURE ANALYSIS; FUNCTIONS; INDUSTRIAL RESEARCH; LEARNING SYSTEMS; MANUFACTURE; PATTERN RECOGNITION; PROBLEM SOLVING; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES;

AUTOMATIC PATTERN RECOGNITION; CLASS IMBALANCE PROBLEMS; CLASSIFICATION RESULTS; CLOUD-BASED; DATA ANALYTICS; MACHINE LEARNING TECHNIQUES; RADIAL BASIS FUNCTIONS; SUPPORT VECTOR MACHINE CLASSIFIERS;

FAULT DETECTION;

EID: 84961198379     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00207543.2016.1153166     Document Type: Article

References (51)

1. G.Andreadis, 2015. “A Collaborative Framework for Social Media Aware Manufacturing.” Manufacturing Letters 3 (3):14–17.10.1016/j.mfglet.2014.11.002
2. R.Barandela, J.S.Sánchez, V.Garcı́a, and E.Rangel. 2003. “Strategies for Learning in Class Imbalance Problems.” Pattern Recognition 36 (3):849–851.10.1016/S0031-3203(02)00257-1
3. S.Barua, M.Islam, X.Yao, and K.Murase. 2014. “MWMOTE-Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning.” IEEE Transactions on Knowledge and Data Engineering 26 (2):405–425.10.1109/TKDE.2012.232
4. G.E.A.P.A.Batista, M.C.Monard, and A.L.C.Bazzan. 2004. “Improving Rule Induction Precision for Automated Annotation by Balancing Skewed Data Sets.” Knowledge Exploration in Life Science Informatics 3303 (3):20–32.10.1007/b103729
5. R.Batuwita, and V.Palade. 2012. “Adjusted Geometric-Mean:A Novel Performance Measure for Imbalanced Bioinformatics Datasets Learning.” Journal of Bioinformatics and Computational Biology 10 (4):1–24.
6. C.M.Bishop, 1995. Neural Networks for Pattern Recognition. Oxford:Oxford University Press.
7. M.Buscema, S.Terzi, and W.Tastle. 2010. “A New Meta-classifier.” In Proceedings of North American Fuzzy Information Processing Society 2 (2):100–107.
8. N.V.Chawla, K.W.Bowyer, L.O.Hall, and W.P.Kegelmeyer. 2002. “SMOTE:Synthetic Minority over-sampling Technique.” Journal of Artificial Intelligence Research 16 (2):21–35.
9. N.Chawla, A.Lazarevic, L.Hall, and K.Bowyer. 2003. “Smoteboost:Improving Prediction of the Minority Class in Boosting.” Knowledge Discovery in Databases:PKDD. 12 (7):107–119.
10. A.K.Choudhury, R.Shankar, and M.K.Tiwari. 2006. “Consensus-based Intelligent Group Decision-making Model for the Selection of Advanced Technology.” Decision Support Systems 42 (3):1776–1799.10.1016/j.dss.2005.05.001
11. A.K.Choudhary, J.A.Harding, and M.K.Tiwari. 2009. “Data Mining in Manufacturing:A Review Based on the Kind of Knowledge.” Journal of Intelligent Manufacturing 20 (5):501–521.10.1007/s10845-008-0145-x
12. S.Choudhury, S.Ghosh, A.Bhattacharya, and K.J.Fernandes. 2014. “A Real Time Clustering and SVM Based Price-Volatility Prediction for Optimal Trading Strategy.” Neurocomputing 131 (5):419–426.10.1016/j.neucom.2013.10.002
13. S.Deng, S.Y.Lin, and W.L.Chang. 2011. “Application of Multiclass Support Vector Machines for Fault Diagnosis of Field Air Defense Gun.” Expert Systems with Applications 38 (5):6007–6013.10.1016/j.eswa.2010.11.020
14. L.Dong, D.Xiao, Y.Liang, and Y.Liu. 2008. “Rough Set and Fuzzy Wavelet Neural Network Integrated with Least Square Weighted Fusion Algorithm Based Fault Diagnosis Research for Power Transformers.” Electric Power Systems Research 78 (1):129–136.10.1016/j.epsr.2006.12.013
15. A.Dutta, C.Guo, A.Peng, and A.K.Choudhary. 2013. “Risks in Enterprise Cloud Computing:The Perspective of It Experts.” Journal of Computer Information Systems 53 (4):39–48.
16. R.Eslamloueyan, 2011. “Designing a Hierarchical Neural Network Based on Fuzzy Clustering for Fault Diagnosis of the Tennessee-Eastman Process.” Applied Soft Computing 11 (1):1407–1415.10.1016/j.asoc.2010.04.012
17. M.A.H.Farquad, and I.Bose. 2012. “Preprocessing Unbalanced Data Using Support Vector Machine.” Decision Support Systems 53 (1):226–233.10.1016/j.dss.2012.01.016
18. M.A.H.Farquad, V.Ravi, and S.B.Raju. 2014. “Churn Prediction Using Comprehensible Support Vector Machine:An Analytical CRM Application.” Applied Soft Computing 19 (1):31–40.10.1016/j.asoc.2014.01.031
19. A.Fernández, V.López, M.Galar, M.J.del Jesus, and F.Herrera 2013. “Analysing the Classification of Imbalanced Data-Sets with Multiple Classes:Binarization Techniques and Ad Hoc Approaches.” Knowledge-Based System 42 (1):97–110.
20. A.Frank, and A.Asuncion. 2010. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA:University of California, School of Information and Computer Science.
21. G.Goodwin,. 2014. “Attitudes on How Big Data will Affect Manufacturing Performance.” http://www.blog.lnsresearch.com/.
22. H.Guo, and H.L.Viktor. 2004. “Learning from Imbalanced Data Sets with Boosting and Data Generation.” ACM SIGKDD Explorations Newsletter 6 (1):30–39.10.1145/1007730
23. H.Han, W.Y.Wang, and B.H.Mao 2005. “Borderline-SMOTE:A New Over-sampling Method in Imbalanced Data Sets Learning.” Proceedings of the International Conference on Intelligent Computing, Part I, Hefei, China, 878–887.
24. H.He, and E.A.Garcia. 2009. “Learning from Imbalanced Data.” IEEE Transactions on Knowledge and Data Engineering 21 (2):1263–1284.
25. S.Hido, H.Kashima, and Y.Takahashi. 2009. “Roughly Balanced Bagging for Imbalanced Data.” Statistical Analysis and Data Mining 2 (5–6):412–426.10.1002/sam.v2:5/6
26. K.Huang, H.Yang, I.King, and M.R.Lyu 2004. “Learning Classifiers from Imbalanced Data Based on Biased Minimax Probability Machine.” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, 558–563.
27. T.Japkowicz, and N.Jo. 2004. “Class Imbalances versus Small Disjuncts.” SIGKDD Explorations 6 (1):40–49.
28. N.Khilwani, A.Prakash, Shankar, and M.K.Tiwari. 2007. “Fast Clonal Algorithm.” Engineering Applications of Artificial Intelligence 21 (2008):106–128.
29. M.Kubat, and S.Matwin. 1997. “Addressing the Curse of Imbalanced Training Sets:One Sided Selection.” 14th International Conference on Machine Learning, Nashville, TN, USA, 179–186.
30. A.Kumar, Prakash, M.K.Tiwari, R.Shankar and A.Baveja. 2005. “Solving Machine Loading Problem of a Flexible Manufacturing System with Constraint-Based Genetic Algorithm.” European Journal of Operation Research 175 (2):1043–1069.
31. S.Kumar, A.K.Choudhary, M.Kumar, and R.Shankar. 2006. “Kernel Distance-based Robust Support Vector Methods and Its Application in Developing a Robust K-chart.” International Journal of Production Research 44 (1):77–96.10.1080/00207540500216037
32. D.Larose, 2005. Discovering Knowledge in Data:An Introduction to Data Mining. Hoboken, NJ:Wiley.
33. C.K.Lau, Y.S.Heng, M.A.Hussain, and M.I.Mohamad Nor. 2010. “Fault Diagnosis of the Polypropylene Production Process (UNIPOL PP) Using ANFIS.” ISA Transactions 49 (4):559–566.10.1016/j.isatra.2010.06.007
34. J.Lee, and E.Lapira. 2013. “Predictive Factories:The Next Transformation.” Manufacturing Leadership Journal 20 (1):13–24.
35. J.Lee, E.Lapira, S.Yang, and H.A.Kao. 2013. “Predictive Manufacturing System Trends of Next Generation Production Systems.” Proceedings of the 11th IFAC Workshop on Intelligent Manufacturing Systems 11 (1):150–156.
36. J.Lee, B.Bagheri, and H.A.Kao. 2015. “A Cyber-physical Systems Architecture for Industry 4.0-Based Manufacturing Systems.” Manufacturing Letters 3 (1):18–23.10.1016/j.mfglet.2014.12.001
37. B.H.Li, L.Zhang, S.L.Wang, F.Tao, J.W.Cao, X.D.Jiang, X.Song, and X.D.Chai. 2010. “Cloud Manufacturing:A New Service-oriented Networked Manufacturing Model.” Computer Integrated Manufacturing Systems 16 (1):1–7.
38. X.Y.Liu, J.Wu, and Z.H.Zhou. 2009. “Exploratory Undersampling for Class Imbalance Learning.” IEEE Transactions Systems Man Cabernets Part B 39 (2):539–550.
39. C.H.Lo, Y.K.Wong, A.B.Rad, and K.M.Chow. 2002. “Fusion of Qualitative Bond Graph and Genetic Algorithms:A Fault Diagnosis Application.” ISA Transactions 41 (2):445–456.
40. N.K.Mishra, A.K.Choudhary, R.Shankar, and M.K.Tiwari. 2009. “Rollout Strategy-based Probabilistic Causal Model for Multiple Fault Diagnosis.” Robotics and Computer Integrated Manufacturing 26 (3):325–332.
41. K.Rajagopal, and S.Anand. 1999. “Assessment of Circularity Error Using a Selective Data Partition Approach.” International Journal of Production Research 37 (17):3959–3979.10.1080/002075499189862
42. V.Ravi, R.Shankar, and M.K.Tiwari. 2005. “Productivity Improvement of a Computer Hardware Supply Chain.” International Journal of Productivity and Performance Management 54 (4):239–255.10.1108/17410400510593802
43. S.Río, V.López, J.M.Benítez, and F.Herrera. 2014. “On the Use of Map Reduce for Imbalanced Big Data Using Random Forest.” Information Science. 285 (1):112–137.
44. C.Seiffert, T.M.Khoshgoftaar, J.Hulse, and A.Napolitano. 2010. “RUSBoost:A Hybrid Approach to Alleviating Class Imbalance.” IEEE Transaction System Man & Cybernetics Part a 40 (1):185–197.
45. S.Simani, and C.Fantuzzi. 2000. “Fault Diagnosis in Power Plant Using Neural Networks.” Information. Sciences 127 (3–4):125–136.10.1016/S0020-0255(00)00034-7
46. M.K.Tiwari, and N.K.Vidyarthi. 2000. “Solving Machine Loading Problems in a Flexible Manufacturing System Using a Genetic Algorithm Based Heuristic Approach.” International Journal of Production Research 38 (14):3357–3384.10.1080/002075400418298
47. C.Y.Tsai, and S.H.Huang. 2015. “A Data Mining Approach to Optimise Shelf Space Allocation in Consideration of Customer Purchase and Moving Behaviours.” International Journal of Production Research 53 (3):850–856.
48. B.X.Wang, and N.Japkowicz. 2008. “Boosting Support Vector Machines for Imbalanced Data Sets.” Lecture Notes Artificial Intelligence 49 (9):38–47.
49. Y.Wang, C.Deng, J.Wu, and Y.Xiong. 2015. “Failure Time Prediction for Mechanical Device Based on the Degradation Sequence.” Journal of Intelligent Manufacturing 6 (1):1–19.
50. D.Wu, D.W.Rosen, L.Wang, and D.Schaefer. 2015. “Cloud-based Design and Manufacturing:A New Paradigm in Digital Manufacturing and Design Innovation.” Computer-Aided Design 59 (1):1–14.
51. X.Xu, 2012. “From Cloud Computing to Cloud Manufacturing.” Robotics and Computer-Integrated Manufacturing 28 (1):75–86.10.1016/j.rcim.2011.07.002