Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms

Automation in Construction - Tập 112 - Trang 103087 - 2020
Jack C.P. Cheng1, Weiwei Chen1, Keyu Chen1, Qian Wang2
1Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
2Department of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore

Tóm tắt

Từ khóa


Tài liệu tham khảo

Eastman, 2011

Mobley, 2002

Hao, 2010, A decision support system for integrating corrective maintenance, preventive maintenance, and condition-based maintenance, 470

Wang, 2017, A new paradigm of cloud-based predictive maintenance for intelligent manufacturing, J. Intell. Manuf., 28, 1125, 10.1007/s10845-015-1066-0

Ren, 2015, A predictive maintenance method for products based on big data analysis, 385

Alexis

Su, 2011, Enhancing maintenance management using building information modeling in facilities management, 752

Motawa, 2013, A knowledge-based BIM system for building maintenance, Autom. Constr., 29, 173, 10.1016/j.autcon.2012.09.008

Shen, 2012, A loosely coupled system integration approach for decision support in facility management and maintenance, Autom. Constr., 25, 41, 10.1016/j.autcon.2012.04.003

Motamedi, 2014, Knowledge-assisted BIM-based visual analytics for failure root cause detection in facilities management, Autom. Constr., 43, 73, 10.1016/j.autcon.2014.03.012

Chen, 2018, BIM-based framework for automatic scheduling of facility maintenance work orders, Autom. Constr., 91, 15, 10.1016/j.autcon.2018.03.007

Kang, 2015, A study on software architecture for effective BIM/GIS-based facility management data integration, Autom. Constr., 54, 25, 10.1016/j.autcon.2015.03.019

Koch, 2014, Natural markers for augmented reality-based indoor navigation and facility maintenance, Autom. Constr., 48, 18, 10.1016/j.autcon.2014.08.009

Lee, 2011, Augmented reality-based computational fieldwork support for equipment operations and maintenance, Autom. Constr., 20, 338, 10.1016/j.autcon.2010.11.004

Cheng, 2017, Comparison of marker-based AR and marker-less AR: a case study on indoor decoration system, 483

Hallberg

Hallberg, 2011, On the use of open bim and 4d visualisation in a predictive life cycle management system for construction works, Journal of Information Technology in Construction (ITcon), 16, 445

Cheng, 2016, A BIM-based decision support system framework for predictive maintenance management of building facilities, 711

Civerchia, 2017, Industrial Internet of Things monitoring solution for advanced predictive maintenance applications, J. Ind. Inf. Integr., 7, 4

Schmidt, 2016, Cloud-enhanced predictive maintenance, Int. J. Adv. Manuf. Technol.

Wang, 2017, How AI affects the future predictive maintenance: a primer of deep learning, 1

Gombé, 2019, A SAW wireless sensor network platform for industrial predictive maintenance, J. Intell. Manuf., 30, 1617, 10.1007/s10845-017-1344-0

Francis, 2019, ARIMA model based real time trend analysis for predictive maintenance, 735

Hopfield, 1982, Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci., 79, 2554, 10.1073/pnas.79.8.2554

J. Luxhøj, An artificial neural network for nonlinear estimation of the turbine flow-meter coefficient, Eng. Appl. Artif. Intell. 11 (6) (1998) 723–734, https://doi.org/10.1016/S0952-1976(98)00016-5.

Tse, 1999, Prediction of machine deterioration using vibration based fault trends and recurrent neural networks, J. Vib. Acoust., 121, 355, 10.1115/1.2893988

J. Shao, Application of an artificial neural network to improve short-term road ice forecasts, Expert Syst. Appl. 14 (4) (1998) 471–482, https://doi.org/10.1016/S0957-4174(98)00006-2.

El-Abbasy, 2014, Artificial neural network models for predicting condition of offshore oil and gas pipelines, Autom. Constr., 45, 50, 10.1016/j.autcon.2014.05.003

Silva, 2013, Statistical models applied to service life prediction of rendered façades, Autom. Constr., 30, 151, 10.1016/j.autcon.2012.11.028

Sousa, 2014, Evaluation of artificial intelligence tool performance and uncertainty for predicting sewer structural condition, Autom. Constr., 44, 84, 10.1016/j.autcon.2014.04.004

Morcous, 2006, Performance prediction of bridge deck systems using Markov chains, J. Perform. Constr. Facil., 20, 146, 10.1061/(ASCE)0887-3828(2006)20:2(146)

Mauer, 2019, 79

Carvalho, 2019, A systematic literature review of machine learning methods applied to predictive maintenance, Comput. Ind. Eng., 137, 106024, 10.1016/j.cie.2019.106024

BSI

Flores-Colen, 2009, Discussion of criteria for prioritization of predictive maintenance of building façades: survey of 30 experts, J. Perform. Constr. Facil., 24, 337, 10.1061/(ASCE)CF.1943-5509.0000104

Niu, 2010, Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance, Reliability Engineering & System Safety, 95, 786, 10.1016/j.ress.2010.02.016

Bansal, 2004, A real-time predictive maintenance system for machine systems, Int. J. Mach. Tools Manuf., 44, 759, 10.1016/j.ijmachtools.2004.02.004

Teicholz, 2013

Newman, 1996, Integrating building automation and control products using the BACnet protocol, ASHRAE J., 38, 36

Chen, 2018, Towards an ontology-based approach for information interoperability between BIM and facility management, 447

Lewis, 2007

Ashworth, 1996, Estimating the life expectancies of building components in life-cycle costing calculations, Struct. Surv., 14, 4, 10.1108/02630809610122730

Chew, 2003, Maintainability problems of wet areas in high-rise residential buildings, Build. Res. Inf., 31, 60

Uzarski, 2007, Knowledge-based condition survey inspection concepts, J. Infrastruct. Syst., 13, 72, 10.1061/(ASCE)1076-0342(2007)13:1(72)

Zhang, 1998, Forecasting with artificial neural networks: the state of the art, Int. J. Forecast., 14, 35, 10.1016/S0169-2070(97)00044-7

Bhasin, 2004, Prediction of CTL epitopes using QM, SVM and ANN techniques, Vaccine, 22, 3195, 10.1016/j.vaccine.2004.02.005

Awodele

Silva, 2011, Service life prediction models for exterior stone cladding, Building Research & Information, 39, 637, 10.1080/09613218.2011.617095

Freitag, 2009, Lifetime prediction using accelerated test data and neural networks, Comput. Struct., 87, 1187, 10.1016/j.compstruc.2008.12.007

Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018

Vapnik, 2015, 11

Vapnik, 2013

Widodo, 2007, Support vector machine in machine condition monitoring and fault diagnosis, Mech. Syst. Signal Process., 21, 2560, 10.1016/j.ymssp.2006.12.007

Hao, 2010, A decision support system for integrating corrective maintenance, preventive maintenance and condition-based maintenance, Proceedings of Construction Research Congress, 8

Umiliacchi, 2011, Predictive maintenance of railway subsystems using an ontology based modelling approach, 22

Maleki, 2017, A tailored ontology supporting sensor implementation for the maintenance of industrial machines, Sensors, 17, 2063, 10.3390/s17092063

Agarwal, 2016, Unified IoT ontology to enable interoperability and federation of testbeds, 70