Access the full text.
Sign up today, get DeepDyve free for 14 days.
Chunhui Zhao (2014)
Fault subspace selection and analysis of relative changes based reconstruction modeling for multi-fault diagnosisThe 26th Chinese Control and Decision Conference (2014 CCDC)
M. Movahhedy, P. Mosaddegh (2006)
Prediction of chatter in high speed milling including gyroscopic effectsInternational Journal of Machine Tools & Manufacture, 46
A. Jardine, Daming Lin, D. Banjevic (2006)
A review on machinery diagnostics and prognostics implementing condition-based maintenanceMechanical Systems and Signal Processing, 20
K. Wang (2014)
Key Techniques in Intelligent Predictive Maintenance (IPdM) – A Framework of Intelligent Faults Diagnosis and Prognosis System (IFDaPS)Advanced Materials Research, 1039
P. Perner (2002)
Data Mining - Concepts and TechniquesKünstliche Intell., 16
Mahdi Sparham, A. Sarhan, N. Mardi, M. Dahari, M. Hamdi (2016)
Cutting force analysis to estimate the friction force in linear guideways of CNC machineMeasurement, 85
A. Siddique, G. Yadava, B. Singh (2003)
Applications of artificial intelligence techniques for induction machine stator fault diagnostics: review4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003.
A. Arslan, C. Colak, M. Sarihan (2016)
Different medical data mining approaches based prediction of ischemic strokeComputer methods and programs in biomedicine, 130
LW Chaves, Z Nochta (2011)
Unique radio innovation for the 21st century
Kevin Murphy (2012)
Machine learning - a probabilistic perspective
D. Ranasinghe, Quan Sheng, S. Zeadally (2010)
Unique Radio Innovation for the 21st Century: Building Scalable and Global RFID Networks, 1
Ching-Wei Wu, Chia-Hui Tang, Ching-Feng Chang, Y. Shiao (2012)
Thermal error compensation method for machine centerThe International Journal of Advanced Manufacturing Technology, 59
G. Vachtsevanos, F. Lewis, M. Roemer, A. Hess, Biqing Wu (2006)
Intelligent Fault Diagnosis and Prognosis for Engineering Systems
Leonardo Chaves, Zoltán Nochta (2011)
Breakthrough Towards the Internet of Things
S. Sumathi, S. Sivanandam (2006)
Introduction to Data Mining and its Applications, 29
Z. Li, Kesheng Wang, Y. He (2016)
Industry 4.0 - Potentials for Predictive Maintenance
J. Duro, J. Padget, C. Bowen, H. Kim, Aydin Nassehi (2016)
Multi-sensor data fusion framework for CNC machining monitoringMechanical Systems and Signal Processing, 66
Kuo Liu, Mingjia Sun, T. Zhu, Yu-liang Wu, Yu Liu (2016)
Modeling and compensation for spindle's radial thermal drift error on a vertical machining centerInternational Journal of Machine Tools & Manufacture, 105
J. Son, Gang Niu, Bo-Suk Yang, D. Hwang, D. Kang (2009)
Development of smart sensors system for machine fault diagnosisExpert Syst. Appl., 36
(2012)
Towards a next generation of manufacturing: zero-defect manufacturing (ZDM) using data mining approaches
N. Girija, .. S.K.Srivatsa (2006)
A Research Study: Using Data Mining in Knowledge Base Business StrategiesInformation Technology Journal, 5
K. Wang (2016)
Intelligent Predictive Maintenance ( IPdM ) System – Industry 4.0 ScenarioWIT transactions on engineering sciences, 113
(2014)
Industrie 4.0: hit or hype
Stefano Zanero (2016)
Cyber-Physical SystemsCybernetics and Systems Analysis, 53
C.C. Wang, Y. Kang, Y. Chung (2012)
The Gearbox Fault Detection of Machine Center by Using Time Frequency Order SpectrumAdvanced Materials Research, 452-453
S. Abbasion, A. Rafsanjani, A. Farshidianfar, N. Irani (2007)
Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machineMechanical Systems and Signal Processing, 21
Kesheng Wang (2014)
Intelligent and integrated RFID (II-RFID) system for improving traceability in manufacturingAdvances in Manufacturing, 2
R Baheti, H Gill (2011)
Cyber-physical systemsImpact Control Technol, 12
(2014)
Human-machineinteraction in the Industry 4.0 era
T. Perl (2009)
Glossary of terms.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists, 66 5 Suppl 3
R Kohavi, F Provost (1998)
Glossary of termsMach Learn, 30
Jay Lee, Hung-An Kao, Shanhu Yang (2014)
Service Innovation and Smart Analytics for Industry 4.0 and Big Data EnvironmentProcedia CIRP, 16
Pan Deng, Gang Ren, Wei Yuan, Feng Chen, Qingsong Hua (2015)
An integrated framework of formal methods for interaction behaviors among industrial equipmentsMicroprocess. Microsystems, 39
Shengyu Shi, Jing Lin, Xiufeng Wang, Xue Xiaoqiang (2015)
Analysis of the transient backlash error in CNC machine tools with closed loopsInternational Journal of Machine Tools & Manufacture, 93
M. Sohrabi, Soodeh Akbari (2016)
A comprehensive study on the effects of using data mining techniques to predict tie strengthComput. Hum. Behav., 60
Meng Gan, Cong Wang, Chang'an Zhu (2016)
Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearingsMechanical Systems and Signal Processing, 72
L. Affonso (2006)
Machinery Failure Analysis Handbook: Sustain Your Operations and Maximize Uptime
C. Zamfirescu, Bogdan-Constantin Pârvu, J. Schlick, D. Zühlke (2013)
Preliminary Insides for an Anthropocentric Cyber-physical Reference Architecture of the Smart FactoryStudies in Informatics and Control, 22
C. Bort, M. Leonesio, P. Bosetti (2016)
A model-based adaptive controller for chatter mitigation and productivity enhancement in CNC milling machinesRobotics and Computer-integrated Manufacturing, 40
Kai Fan, Jian-guo Yang, Liyan Yang (2015)
Unified error model based spatial error compensation for four types of CNC machining center: Part I—Singular function based unified error modelMechanical Systems and Signal Processing, 60
P. Rodríguez, J. Alonso, M. Ferrer-Ballester, C. Travieso-González (2014)
Review of Automatic Fault Diagnosis Systems Using Audio and Vibration SignalsIEEE Transactions on Systems, Man, and Cybernetics: Systems, 44
Lorena Siguenza-Guzman, Víctor Saquicela, Elina Avila-Ordóñez, J. Vandewalle, D. Cattrysse (2015)
Literature Review of Data Mining Applications in Academic LibrariesThe Journal of Academic Librarianship, 41
A. Horenbeek, L. Pintelon (2013)
A dynamic predictive maintenance policy for complex multi-component systemsReliab. Eng. Syst. Saf., 120
Zuriani Usop, A. Sarhan, N. Mardi, Nizam Wahab (2015)
Measuring of positioning, circularity and static errors of a CNC Vertical Machining Centre for validating the machining accuracyMeasurement, 61
Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the accuracy and reliability of fault diagnosis and prognosis via data mining remains a prominent issue in this field. This study investigates fault diagnosis and prognosis in machine centers based on data mining approaches to formulate a systematic approach and obtain knowledge for predictive maintenance in Industry 4.0 era. We introduce a system framework based on Industry 4.0 concepts, which includes the process of fault analysis and treatment for predictive maintenance in machine centers. The framework includes five modules: sensor selection and data acquisition module, data preprocessing module, data mining module, decision support module, and maintenance implementation module. Furthermore, a case study is presented to illustrate the application of the data mining methods for fault diagnosis and prognosis in machine centers as an Industry 4.0 scenario.
Advances in Manufacturing – Springer Journals
Published: Dec 5, 2017
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.