A Review of Data Mining Technologies for Condition Based Monitoring for Machine Tools


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Looking at the high rates of production and the steep competition in the world market, it becomes quite essential that the fault control is done in a very efficient way. This article presents a summary on the maintenance, the monitoring techniques, and the diagnosis methods for the condition based maintenance of machine tools. The paper initially gives a brief introduction on the condition based maintenance of machine tools. In the next part, the various methods for the monitoring are discussed followed by the models for data mining. The paper concludes that most of the techniques have their own advantages and drawbacks, so a careful selection of the techniques is needed to form a proper monitoring system.



Edited by:

Kesheng Wang, Jan Ola Strandhagen and Dawei Tu






K. S. Wang et al., "A Review of Data Mining Technologies for Condition Based Monitoring for Machine Tools", Advanced Materials Research, Vol. 1039, pp. 155-162, 2014

Online since:

October 2014




* - Corresponding Author

[1] R. Ahmad, S. Kamaruddin, An overview of time-based and condition-based maintenance in industrial application, Computers & Industrial Engineering, 63 (2012) 135-149.

DOI: 10.1016/j.cie.2012.02.002

[2] A. Dawn, C. Joo Ho, K. Nam Ho, Options for Prognostics Methods: A review of data-driven and physics-based prognostics, 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, American Institute of Aeronautics and Astronautics2013.

DOI: 10.2514/6.2013-1940

[3] K. Wang, V.S. Sharma, Z. Zhang, SCADA data interpretation for condition-based monitoring of wind turbines, NTNU, Faculty of Engineering Science and Technology, Trondheim, 2013, pp. S. 307-322.

[4] A.K.S. Jardine, D. Lin, D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, 20 (2006) 1483-1510.

DOI: 10.1016/j.ymssp.2005.09.012

[5] G. Byrne, D. Dornfeld, I. Inasaki, G. Ketteler, W. König, R. Teti, Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application, CIRP Annals - Manufacturing Technology, 44 (1995) 541-567.

DOI: 10.1016/s0007-8506(07)60503-4

[6] M.C. Carnero, R. González-Palma, D. Almorza, P. Mayorga, C. López-Escobar, Statistical quality control through overall vibration analysis, Mechanical Systems and Signal Processing, 24 (2010) 1138-1160.

DOI: 10.1016/j.ymssp.2009.09.007

[7] J. Cibulka, M.K. Ebbesen, G. Hovland, K.G. Robbersmyr, M.R. Hansen, A Review on Approaches for Condition Based Maintenance in Applications with Induction Machines Located Offshore, Modeling Identification and Control, 33 (2012) 69-86.

DOI: 10.4173/mic.2012.2.4

[8] M.E. Elnady, J.K. Sinha, S.O. Oyadiji, Condition monitoring of rotating machines using on-shaft vibration measurement, 10th International Conference on Vibrations in Rotating Machinery, Woodhead Publishing2012, pp.669-678.

DOI: 10.1533/9780857094537.10.669

[9] A. Deraemaeker, Vibration Based Structural Health Monitoring Using Large Sensor Arrays: Overview of Instrumentation and Feature Extraction Based on Modal Filters, in: A. Deraemaeker, K. Worden (Eds. ) New Trends in Vibration Based Structural Health Monitoring, Springer Vienna2010, pp.19-54.

DOI: 10.1007/978-3-7091-0399-9_2

[10] M. Sparham, A.D. Sarhan, N.A. Mardi, M. Hamdi, Designing and manufacturing an automated lubrication control system in CNC machine tool guideways for more precise machining and less oil consumption, The International Journal of Advanced Manufacturing Technology, 70 (2014).

DOI: 10.1007/s00170-013-5325-y

[11] R. Greenough, T. Grubic, Modelling condition-based maintenance to deliver a service to machine tool users, The International Journal of Advanced Manufacturing Technology, 52 (2011) 1117-1132.

DOI: 10.1007/s00170-010-2760-x

[12] H. -E. Kim, A.C. Tan, J. Mathew, E.H. Kim, B. -K. Choi, Machine Prognostics Based on Health State Estimation Using SVM, in: J.E. Amadi-Echendu, R. Willett, K. Brown, J. Mathew (Eds. ) Asset Condition, Information Systems and Decision Models, Springer London2012, pp.169-186.

DOI: 10.1007/978-1-4471-2924-0_9

[13] J. Lee, J. Ni, D. Djurdjanovic, H. Qiu, H. Liao, Intelligent prognostics tools and e-maintenance, Computers in Industry, 57 (2006) 476-489.

DOI: 10.1016/j.compind.2006.02.014

[14] A.R. Motorcu, A. Güllü, Statistical process control in machining, a case study for machine tool capability and process capability, Materials & Design, 27 (2006) 364-372.

DOI: 10.1016/j.matdes.2004.11.003

[15] Y. Peng, M. Dong, M. Zuo, Current status of machine prognostics in condition-based maintenance: a review, The International Journal of Advanced Manufacturing Technology, 50 (2010) 297-313.

[16] A. Verl, U. Heisel, M. Walther, D. Maier, Sensorless automated condition monitoring for the control of the predictive maintenance of machine tools, CIRP Annals - Manufacturing Technology, 58 (2009) 375-378.

DOI: 10.1016/j.cirp.2009.03.039

[17] R.A. Saeed, A.N. Galybin, V. Popov, 3D fluid–structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS, Mechanical Systems and Signal Processing, 34 (2013) 259-276.

DOI: 10.1016/j.ymssp.2012.08.004

[18] R. Ji-Hong, C. Jiang-Cheng, W. Nan, Visual Analysis of SOM Network in Fault Diagnosis, Physics Procedia, 22 (2011) 333-338.

DOI: 10.1016/j.phpro.2011.11.052

[19] T. Kobayashi, D.L. Simon, Hybrid Neural-Network Genetic-Algorithm Technique for Aircraft Engine Performance Diagnostics, Journal of Propulsion and Power, 21 (2005) 751-758.

DOI: 10.2514/1.9881

[20] A.S.N. Huda, S. Taib, K.H. Ghazali, M.S. Jadin, A new thermographic NDT for condition monitoring of electrical components using ANN with confidence level analysis, ISA Transactions, 53 (2014) 717-724.

DOI: 10.1016/j.isatra.2014.02.003

[21] A. Najah, A. El-Shafie, O.A. Karim, A. El-Shafie, Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring, Environ Sci Pollut Res, 21 (2014) 1658-1670.

DOI: 10.1007/s11356-014-3813-8

[22] W. -K. Wong, C. -K. Loo, W. -S. Lim, P. -N. Tan, Thermal condition monitoring system using log-polar mapping, quaternion correlation and max-product fuzzy neural network classification, Neurocomputing, 74 (2010) 164-177.

DOI: 10.1016/j.neucom.2010.02.027

[23] Q. Ren, M. Balazinski, L. Baron, K. Jemielniak, R. Botez, S. Achiche, Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling, Information Sciences, 255 (2014) 121-134.

DOI: 10.1016/j.ins.2013.06.010

[24] R. Kothamasu, S.H. Huang, Adaptive Mamdani fuzzy model for condition-based maintenance, Fuzzy Sets and Systems, 158 (2007) 2715-2733.

DOI: 10.1016/j.fss.2007.07.004

[25] A. Widodo, B. -S. Yang, Support vector machine in machine condition monitoring and fault diagnosis, Mechanical Systems and Signal Processing, 21 (2007) 2560-2574.

DOI: 10.1016/j.ymssp.2006.12.007

[26] P. Konar, P. Chattopadhyay, Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs), Applied Soft Computing, 11 (2011) 4203-4211.

DOI: 10.1016/j.asoc.2011.03.014

[27] A. Munõz, S. Martorell, V. Serradell, Genetic algorithms in optimizing surveillance and maintenance of components, Reliability Engineering & System Safety, 57 (1997) 107-120.

DOI: 10.1016/s0951-8320(97)00031-8

[28] M. Marseguerra, E. Zio, L. Podofillini, Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation, Reliability Engineering & System Safety, 77 (2002) 151-165.

DOI: 10.1016/s0951-8320(02)00043-1

[29] C.M.F. Lapa, C.M.N.A. Pereira, M.P. de Barros, A model for preventive maintenance planning by genetic algorithms based in cost and reliability, Reliability Engineering & System Safety, 91 (2006) 233-240.

DOI: 10.1016/j.ress.2005.01.004

[30] A. Sanchez, S. Carlos, S. Martorell, J.F. Villanueva, Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization, Reliability Engineering & System Safety, 94 (2009) 22-32.

DOI: 10.1016/j.ress.2007.03.022

[31] M. Klaic, T. Staroveski, T. Udiljak, Tool Wear Classification Using Decision Treesin Stone Drilling Applications: A Preliminary Study, Procedia Engineering, 69 (2014) 1326-1335.

DOI: 10.1016/j.proeng.2014.03.125

[32] M. Amarnath, V. Sugumaran, H. Kumar, Exploiting sound signals for fault diagnosis of bearings using decision tree, Measurement, 46 (2013) 1250-1256.

DOI: 10.1016/j.measurement.2012.11.011

[33] M. Elangovan, S.B. Devasenapati, N.R. Sakthivel, K.I. Ramachandran, Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm, Expert Systems with Applications, 38 (2011).

DOI: 10.1016/j.eswa.2010.09.116

[34] M. Gerdes, Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning, Expert Systems with Applications, 40 (2013) 5021-5026.

DOI: 10.1016/j.eswa.2013.03.025

[35] W. Sun, J. Chen, J. Li, Decision tree and PCA-based fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing, 21 (2007) 1300-1317.

DOI: 10.1016/j.ymssp.2006.06.010

[36] W. Sammouri, E. Côme, L. Oukhellou, P. Aknin, Mining Floating Train Data Sequences for Temporal Association Rules within a Predictive Maintenance Framework, in: P. Perner (Ed. ) Advances in Data Mining. Applications and Theoretical Aspects, Springer Berlin Heidelberg2013, pp.112-126.

DOI: 10.1007/978-3-642-39736-3_9

[37] C. Tong, P. Guo, Data mining with improved Apriori algorithm on wind generator alarm data, 2013 25th Chinese Control and Decision Conference, CCDC 2013Guiyang, 2013, p.1936-(1941).

DOI: 10.1109/ccdc.2013.6561250

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