Prognostics 101: a tutorial for particle filter-based prognostics algorithm using Matlab

A New Approach to Linear Filtering and Prediction Problems

Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751 · see citing works

A Critical Analysis of Crack Propagation Laws

Paris, P. C., & Erdoğan, F. (1963) · Journal of Basic Engineering · doi:10.1115/1.3656900 · Cited by 7,168 · see citing works

LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S

Bayes, T. (1763) · Philosophical Transactions of the Royal Society of London · doi:10.1098/rstl.1763.0053 · Cited by 3,023 · see citing works

AN ESSAY TOWARDS SOLVING A PROBLEM IN THE DOCTRINE OF CHANCES

BAYES (1958) · Biometrika · doi:10.1093/biomet/45.3-4.296 · Cited by 2,939 · see citing works

Intelligent Fault Diagnosis and Prognosis for Engineering Systems

Vachtsevanos, G., Lewis, F. L., Roemer, M. J., Hess, A. J., & Wu, B. (2006) · Wiley eBooks · doi:10.1002/9780470117842 · Cited by 1,134 · see citing works

Prognostics in Battery Health Management

Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2008.4579269 · Cited by 594 · see citing works

A particle-filtering approach for on-line fault diagnosis and failure prognosis

Orchard, M. E., & Vachtsevanos, G. (2009) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331208092026 · Cited by 464 · see citing works

Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

Hu, C. K., Youn, B. D., Wang, P., & Yoon, J. T. (2012) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2012.03.008 · Cited by 371 · see citing works

Particle filtering prognostic estimation of the remaining useful life of nonlinear components

Zio, E., & Peloni, G. (2010) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2010.08.009 · Cited by 341 · see citing works

A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system

Zio, E., & Maio, F. D. (2009) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2009.08.001 · Cited by 335 · see citing works

On some properties of Markov chain Monte Carlo simulation methods based on the particle filter

Pitt, M. K., Silva, R. D. S., Giordani, P. E., & Kohn, R. (2012) · Journal of Econometrics · doi:10.1016/j.jeconom.2012.06.004 · Cited by 266 · see citing works

Model-Based Prognostic Techniques Applied to a Suspension System

Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2008.2001055 · Cited by 234 · see citing works

Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

Hu, C. K., Youn, B. D., & Wang, P. (2011) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2011.6024361 · Cited by 205 · see citing works

An engineering model of fatigue crack growth under variable amplitude loading

Huang, X., TORGEIR, M., & Cui, W. (2007) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2007.03.004 · Cited by 202 · see citing works

A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue

Chookah, M., Nuhi, M., & Modarres, M. (2011) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2011.07.007 · Cited by 78 · see citing works

Multiple damage progression paths in model-based prognostics

Daigle, M. J., & Goebel, K. (2011) · doi:10.1109/aero.2011.5747574 · Cited by 60 · see citing works

Identification of correlated damage parameters under noise and bias using Bayesian inference

An, D., Choi, J.-H., & Kim, N. H. (2011) · Structural Health Monitoring · doi:10.1177/1475921711424520 · Cited by 59 · see citing works

Parameter estimation of railway vehicle dynamic model using rao-blackwellised particle filter

Li, P., Goodall, R. M., & Kadirkamanathan, V. (2003) · European Control Conference (ECC) · doi:10.23919/ecc.2003.7085323 · Cited by 52 · see citing works

A probabilistic-based airframe integrity management model

Wang, X., Rabiei, M., Hurtado, J., Modarres, M., & Hoffman, P. C. (2008) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2008.10.010 · Cited by 48 · see citing works

In situ monitoring and prediction of progressive joint wear using Bayesian statistics

An, D., Choi, J.-H., Schmitz, T. L., & Kim, N. H. (2011) · Wear · doi:10.1016/j.wear.2011.02.010 · Cited by 31 · see citing works

Exact Nonlinear Filtering and Prediction in Process Model-Based Prognostics

DeCastro, J., Tang, L. D., Loparo, K. A., Goebel, K., & Vachtsevanos, G. (2014) · Cited by 20 · see citing works

A Bayesian approach for the estimation of model parameters from noisy data sets

Payne, S. J. (2005) · IEEE Signal Processing Letters · doi:10.1109/lsp.2005.849542 · Cited by 8 · see citing works

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.016 · Cited by 2,634 · see citing works

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193 · see citing works

An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

Li, N., Lei, Y., Lin, J., & Ding, S. X. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2455055 · Cited by 688 · see citing works

A review of the state of health for lithium-ion batteries: Research status and suggestions

Tian, H., Qin, P., Li, K., & Zhao, Z. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120813 · Cited by 654 · see citing works

A Model-Based Method for Remaining Useful Life Prediction of Machinery

Lei, Y., Li, N., Gontarz, S., Lin, J., Radkowski, S., & Dybała, J. (2016) · IEEE Transactions on Reliability · doi:10.1109/tr.2016.2570568 · Cited by 618 · see citing works

Practical options for selecting data-driven or physics-based prognostics algorithms with reviews

An, D., Kim, N. H., & Choi, J.-H. (2014) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2014.09.014 · Cited by 454 · see citing works

Direct Remaining Useful Life Estimation Based on Support Vector Regression

Khelif, R., Chebel‐Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., & Noureddine Zerhouni, A. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2623260 · Cited by 440 · see citing works

Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme

Yu, W., Kim, I. Y., & Mechefske, C. K. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.05.005 · Cited by 406 · see citing works

A review on prognostics and health management (PHM) methods of lithium-ion batteries

Meng, H., & Li, Y. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109405 · Cited by 393 · see citing works

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kan, M. S., Tan, A. C. C., & Mathew, J. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.02.016 · Cited by 381 · see citing works

Degradation Feature Selection for Remaining Useful Life Prediction of Rolling Element Bearings

Zhang, B., Zhang, L., & Xu, J. (2015) · Quality and Reliability Engineering International · doi:10.1002/qre.1771 · Cited by 255 · see citing works

A New Method Based on Stochastic Process Models for Machine Remaining Useful Life Prediction

Lei, Y., Li, N., & Lin, J. (2016) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2016.2601004 · Cited by 253 · see citing works

A Wiener-Process-Model-Based Method for Remaining Useful Life Prediction Considering Unit-to-Unit Variability

Li, N., Lei, Y., Yan, T., Li, N., & Han, T. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2838078 · Cited by 222 · see citing works

Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation

Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Noureddine Zerhouni, A. (2020) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2017.v8i3.2667 · Cited by 225 · see citing works

State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels

Javed, K., Gouriveau, R., & Zerhouni, N. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.01.050 · Cited by 217 · see citing works

An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples

Wang, J., Liang, Y., Zheng, Y., Gao, R. X., & Zhang, F. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.06.103 · Cited by 199 · see citing works

A hybrid remaining useful life prognostic method for proton exchange membrane fuel cell

Cheng, Y., Noureddine Zerhouni, A., & Lü, C. (2018) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2018.04.160 · Cited by 192 · see citing works

A Review on Prognostics Methods for Engineering Systems

Guo, J. H., Li, Z., & Li, M. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2019.2957965 · Cited by 189 · see citing works

A Calibration-Based Hybrid Transfer Learning Framework for RUL Prediction of Rolling Bearing Across Different Machines

Deng, Y., Du, S., Wang, D., Shao, Y., & Huang, D. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3260283 · Cited by 181 · see citing works

Data-driven based fault prognosis for industrial systems: a concise overview

Zhong, K., Han, M., & Han, B. (2019) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2019.1911804 · Cited by 174 · see citing works

Remaining life prediction of lithium-ion batteries based on health management: A review

Song, K., Hu, D., Tong, Y., & Yue, X. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106193 · Cited by 153 · see citing works

Advances in Integrated System Health Management for mission-essential and safety-critical aerospace applications

Ranasinghe, K. C., Sabatini, R., Gardi, A., Bijjahalli, S., Kapoor, R., Fahey, T., & Thangavel, K. (2021) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2021.100758 · Cited by 153 · see citing works

Comparison of a particle filter and other state estimation methods for prognostics of lithium-ion batteries

Walker, E. A., Rayman, S. C., & White, R. E. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.04.020 · Cited by 141 · see citing works

Long-term gear life prediction based on ordered neurons LSTM neural networks

Yan, H., Qin, Y., Xiang, S., Wang, Y., & Chen, H. (2020) · Measurement · doi:10.1016/j.measurement.2020.108205 · Cited by 136 · see citing works

A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery

Tang, T. H., & Yuan, H. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108082 · Cited by 127 · see citing works

Interacting multiple model particle filter for prognostics of lithium-ion batteries

Su, X., Wang, S., PECHT, M. G., Zhao, L., & Ye, Z. (2017) · Microelectronics Reliability · doi:10.1016/j.microrel.2017.02.003 · Cited by 124 · see citing works

Remaining useful life prediction of lithium‐ion battery based on extended Kalman particle filter

Duan, B., Zhang, Q., Geng, F., & Zhang, C. (2019) · International Journal of Energy Research · doi:10.1002/er.5002 · Cited by 122 · see citing works

Remaining Useful Life Prediction Based on a General Expression of Stochastic Process Models

Li, N., Lei, Y., Guo, L., Yan, T., & Lin, J. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2677334 · Cited by 120 · see citing works

Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering

Baptista, M. L., Henriques, E. M. P., de Medeiros, I. P., Malere, J. P., Nascimento, C. L., & Prendinger, H. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.01.017 · Cited by 120 · see citing works

A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression

Pang, X., Liu, X., Jia, J., Wen, J., Shi, Y., Zeng, J., & Zhao, Z. (2021) · Microelectronics Reliability · doi:10.1016/j.microrel.2021.114405 · Cited by 114 · see citing works

A particle filtering and kernel smoothing-based approach for new design component prognostics

Hu, Y., Baraldi, P., Maio, F. D., & Zio, E. (2014) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2014.10.003 · Cited by 116 · see citing works

A review on diagnostic and prognostic approaches for gears

Kundu, P., Darpe, A. K., & Kulkarni, M. S. (2020) · Structural Health Monitoring · doi:10.1177/1475921720972926 · Cited by 114 · see citing works

An Enhanced Deep Learning-Based Fusion Prognostic Method for RUL Prediction

Huang, C., Yin, X., Huang, H., & Li, Y. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2019.2948705 · Cited by 109 · see citing works

Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model

Li, N., Gebraeel, N., Lei, Y., Bian, L., & Si, X. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.02.017 · Cited by 106 · see citing works

Wiener-based remaining useful life prediction of rolling bearings using improved Kalman filtering and adaptive modification

Li, Y., Huang, X., Ding, P., & Zhao, C. (2021) · Measurement · doi:10.1016/j.measurement.2021.109706 · Cited by 104 · see citing works

Prognostic for fuel cell based on particle filter and recurrent neural network fusion structure

Xie, R., Ma, R., Pu, S., Xu, L., Zhao, D., & Huangfu, Y. (2020) · Energy and AI · doi:10.1016/j.egyai.2020.100017 · Cited by 102 · see citing works

A Review of Prognostic Techniques for High-Power White LEDs

Sun, B., Jiang, X., Yung, K. C., Fan, J., & PECHT, M. G. (2016) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2016.2618422 · Cited by 100 · see citing works

CycleGAN for undamaged-to-damaged domain translation for structural health monitoring and damage detection

Luleci, F., Çatbaş, F. N., & Avcı, O. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110370 · Cited by 95 · see citing works

Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

Althubaiti, A., Elasha, F., & Teixeira, J. A. (2021) · Journal of Vibroengineering · doi:10.21595/jve.2021.22100 · Cited by 93 · see citing works

Predicting long-term lumen maintenance life of LED light sources using a particle filter-based prognostic approach

Fan, J., Yung, K. C., & PECHT, M. G. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.10.021 · Cited by 91 · see citing works

Remaining useful life prediction

Lei, Y. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-811534-3.00006-8 · Cited by 88 · see citing works

An enhanced particle filter technology for battery system state estimation and RUL prediction

Ahwiadi, M., & Wang, W. (2022) · Measurement · doi:10.1016/j.measurement.2022.110817 · Cited by 87 · see citing works

Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview

Fu, S., & Avdelidis, N. P. (2023) · Sensors · doi:10.3390/s23198124 · Cited by 112 · see citing works

A survey of the state of condition-based maintenance (CBM) in the nuclear power industry

Ayo-Imoru, R. M., & Cilliers, A. C. (2017) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2017.10.010 · Cited by 80 · see citing works

A machine learning approach to circumventing the curse of dimensionality in discontinuous time series machine data

Aremu, O. O., Hyland-Wood, D., & McAree, P. R. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.106706 · Cited by 79 · see citing works

Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering

Bai, G., Su, Y., Rahman, M. M., & Wang, Z. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108944 · Cited by 76 · see citing works

Remain useful life prediction of rolling bearings based on exponential model optimized by gradient method

Wang, G., & Xiang, J. (2021) · Measurement · doi:10.1016/j.measurement.2021.109161 · Cited by 74 · see citing works

Condition-based prediction of time-dependent reliability in composites

Chiachío, J., Chiachío, M., Sankararaman, S., Saxena, A., & Goebel, K. (2015) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2015.04.018 · Cited by 74 · see citing works

Pipeline condition monitoring towards digital twin system: A case study

Wang, T., Feng, K., Ling, J., Liao, M., Yang, C., Neubeck, R., & Liu, Z. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.02.006 · Cited by 73 · see citing works

Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox

Teng, W., Zhang, X., Liu, Y., Kusiak, A., & Ma, Z. (2016) · Energies · doi:10.3390/en10010032 · Cited by 70 · see citing works

An integrated systemic model for optimization of condition-based maintenance with human error

Asadzadeh, S. M., & Azadeh, A. (2013) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2013.11.008 · Cited by 68 · see citing works

Optimization of nonlinear, non-Gaussian Bayesian filtering for diagnosis and prognosis of monotonic degradation processes

Corbetta, M., Sbarufatti, C., Giglio, M., & Todd, M. D. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.012 · Cited by 68 · see citing works

Prediction of remaining useful life by data augmentation technique based on dynamic time warping

Kim, S., Kim, N. H., & Choi, J.-H. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106486 · Cited by 67 · see citing works

A hybrid approach based on deep neural network and double exponential model for remaining useful life prediction

Liang, J., Liu, H., & Xiao, N. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123563 · Cited by 68 · see citing works

Online Performance Assessment Methodfor a Model-Based Prognostic Approach

Hu, Y., Baraldi, P., Maio, F. D., & Zio, E. (2015) · IEEE Transactions on Reliability · doi:10.1109/tr.2015.2500681 · Cited by 66 · see citing works

Accelerated Stress Factors Based Nonlinear Wiener Process Model for Lithium-Ion Battery Prognostics

Kong, J., Wang, D., Yan, T., Zhu, J., & Zhang, X. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3127035 · Cited by 65 · see citing works

A joint particle filter and expectation maximization approach to machine condition prognosis

Wang, J., Gao, R. X., Zhuang, Y., Fan, Z., & Zhang, L. (2016) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-016-1268-0 · Cited by 65 · see citing works

Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework

Jha, M., Dauphin-Tanguy, G., & Bouamama, B. O. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2016.01.010 · Cited by 64 · see citing works

Emerging technologies in prognostics for fuel cells including direct hydrocarbon fuel cells

Ong, S., Al–Othman, A., & Tawalbeh, M. (2023) · Energy · doi:10.1016/j.energy.2023.127721 · Cited by 62 · see citing works

Machine Learning: Fundamentals

Kang, M., & Jameson, N. J. (2018) · doi:10.1002/9781119515326.ch4 · Cited by 62 · see citing works

Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring

Acuña-Ureta, D. E., & Orchard, M. E. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2016.08.029 · Cited by 61 · see citing works

A Novel Prognostics Approach Using Shifting Kernel Particle Filter of Li-Ion Batteries Under State Changes

Kim, S., Park, H. J., Choi, J.-H., & Kwon, D. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2978688 · Cited by 60 · see citing works

Data-Driven Method for Predicting Remaining Useful Life of Bearing Based on Bayesian Theory

Gao, T., Li, Y., Huang, X., & Wang, C. (2020) · Sensors · doi:10.3390/s21010182 · Cited by 58 · see citing works

A similarity based methodology for machine prognostics by using kernel two sample test

Cai, H., Jia, X., Feng, J., Li, W., Pahren, L., & Lee, J. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.03.007 · Cited by 58 · see citing works

Physics-based prognostic modelling of filter clogging phenomena

Eker, O. F., Camci, F., & Jennions, I. K. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.12.011 · Cited by 54 · see citing works

A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines

Li, X., Yang, X., Yang, Y., Bennett, I. J., & Mba, D. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105564 · Cited by 48 · see citing works

A review of smartphones‐based indoor positioning: Challenges and applications

Nguyen, K. A., Luo, Z., Li, G., & Watkins, C. (2021) · IET Cyber-Systems and Robotics · doi:10.1049/csy2.12004 · Cited by 45 · see citing works

Multidimensional prognostics for rotating machinery: A review

Li, X., Duan, F., Mba, D., & Bennett, I. J. (2017) · Advances in Mechanical Engineering · doi:10.1177/1687814016685004 · Cited by 45 · see citing works

Dual crack growth prognosis by using a mixture proposal particle filter and on-line crack monitoring

Chen, J., Yuan, S., Sbarufatti, C., & Jin, X. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107758 · Cited by 45 · see citing works

Different methods for RUL prediction considering sensor degradation

Hachem, H., Vu, H. C., & Fouladirad, M. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109897 · Cited by 44 · see citing works

Exploring the Relationship Between Data Science and Circular Economy: An Enhanced CRISP-DM Process Model

Kristoffersen, E., Aremu, O. O., Blomsma, F., Mikalef, P., & Li, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29374-1_15 · Cited by 43 · see citing works

A Particle Filter and Long Short-Term Memory Fusion Technique for Lithium-Ion Battery Remaining Useful Life Prediction

Hu, X., Yang, X. P. O., Feng, F., Liu, K., & Lin, X. (2020) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4049234 · Cited by 43 · see citing works

A novel hybrid scheme for remaining useful life prognostic based on secondary decomposition, BiGRU and error correction

Zhu, T., Wang, W., & Yu, M. (2023) · Energy · doi:10.1016/j.energy.2023.127565 · Cited by 41 · see citing works

Microservices-based cloud-edge collaborative condition monitoring platform for smart manufacturing systems

Yang, H., Ong, S. K., Nee, A. Y. C., Jiang, G., & Mei, X. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2098075 · Cited by 40 · see citing works

Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model

Wen, J., Gao, H., & Zhang, J. (2018) · Shock and Vibration · doi:10.1155/2018/4068431 · Cited by 40 · see citing works

Remaining useful life prediction for machinery by establishing scaled-corrected health indicators

Yang, H., Sun, Z., Jiang, G., Zhao, F., & Mei, X. (2020) · Measurement · doi:10.1016/j.measurement.2020.108035 · Cited by 40 · see citing works

A Robust Model-Based Approach for Bearing Remaining Useful Life Prognosis in Wind Turbines

Teng, W., Chen, H., Hu, Y., Cheng, X., Song, L., & Liu, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2978301 · Cited by 39 · see citing works

Stochastic hybrid system approach to task-orientated remaining useful life prediction under time-varying operating conditions

Long, J., Chen, C., Liu, Z., Guo, J., & Chen, W. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108568 · Cited by 38 · see citing works

A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system

Pang, Z., Li, T., Pei, H., & Si, X. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108854 · Cited by 38 · see citing works

Challenges and Opportunities of System-Level Prognostics

Kim, S., Choi, J.-H., & Kim, N. H. (2021) · Sensors · doi:10.3390/s21227655 · Cited by 37 · see citing works

A predictive sliding local outlier correction method with adaptive state change rate determining for bearing remaining useful life estimation

Wang, H., Wang, D., Liu, H., & Tang, G. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108601 · Cited by 37 · see citing works

A novel scalable method for machine degradation assessment using deep convolutional neural network

Pin, L., Jia, X., Feng, J., Zhu, F., Miller, M., Chen, L.-Y., & Lee, J. (2019) · Measurement · doi:10.1016/j.measurement.2019.107106 · Cited by 37 · see citing works

A Tutorial for Feature Engineering in the Prognostics and Health Management of Gears and Bearings

Sim, J., Kim, S., Park, H. J., & Choi, J.-H. (2020) · Applied Sciences · doi:10.3390/app10165639 · Cited by 36 · see citing works

Pump remaining useful life prediction based on multi-source fusion and monotonicity-constrained particle filtering

Yu, H., & Li, H. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.108851 · Cited by 36 · see citing works

Structuring Data for Intelligent Predictive Maintenance in Asset Management

Aremu, O. O., Palau, A. S., Parlikad, A. K., Hyland-Wood, D., & McAree, P. R. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.370 · Cited by 35 · see citing works

A Hybrid Health Prognostics Method for Proton Exchange Membrane Fuel Cells With Internal Health Recovery

Peng, W., Wei, Z., Huang, C., Feng, G., & Li, J. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3243788 · Cited by 35 · see citing works

Comparison of Computational Prognostic Methods for Complex Systems Under Dynamic Regimes: A Review of Perspectives

Bektaş, O., Marshall, J., & Jones, J. A. (2019) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-019-09339-7 · Cited by 34 · see citing works

Color Shift Failure Prediction for Phosphor-Converted White LEDs by Modeling Features of Spectral Power Distribution with a Nonlinear Filter Approach

Fan, J., Mohamed, M. G., Qian, C., Fan, X., Zhang, G., & PECHT, M. G. (2017) · Materials · doi:10.3390/ma10070819 · Cited by 33 · see citing works

An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples

Merainani, B., Laddada, S., Bechhoefer, E. R., Chikh, M. A. A., & Benazzouz, D. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.10.062 · Cited by 33 · see citing works

A particle filtering-based approach for remaining useful life predication of rolling element bearings

Li, N., Lei, Y., Liu, Z., & Lin, J. (2014) · International Conference on Prognostics and Health Management (PHM) · doi:10.1109/icphm.2014.7036367 · Cited by 33 · see citing works

Probabilistic prognosis of fatigue crack growth for asphalt concretes

Lee, S.-J., Zi, G., Mun, S., Kong, J. S., & Choi, J.-H. (2015) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2015.04.033 · Cited by 32 · see citing works

Artificial Fish Swarm Algorithm‐Based Particle Filter for Li‐Ion Battery Life Prediction

Tian, Y., Lü, C., Wang, Z., & Tao, L. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/564894 · Cited by 32 · see citing works

Multi-objective decision-making model based on CBM for an aircraft fleet with reliability constraint

Lin, L., Luo, B., & Zhong, S. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1467574 · Cited by 31 · see citing works

Introduction

Kim, N.-H., An, D., & Choi, J.-H. (2016) · Springer eBooks · doi:10.1007/978-3-319-44742-1_1 · Cited by 31 · see citing works

Lithium-ion batteries remaining useful life prediction using Wiener process and unscented particle filter

Wang, R., & Feng, H. (2019) · Journal of Power Electronics · doi:10.1007/s43236-019-00016-3 · Cited by 31 · see citing works

A Probabilistic Framework for Remaining Useful Life Prediction of Bearings

Wang, T., Liu, Z., & Mrad, N. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3029382 · Cited by 30 · see citing works

Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

Saïdi, L., & Benbouzid, M. (2021) · Electronics · doi:10.3390/electronics10222732 · Cited by 30 · see citing works

Creation of Publicly Available Data Sets for Prognostics and Diagnostics Addressing Data Scenarios Relevant to Industrial Applications

Mauthe, F., Hagmeyer, S., & Zeiler, P. (2021) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2021.v12i2.3087 · Cited by 30 · see citing works

Prognostics of lithium-ion batteries based on different dimensional state equations in the particle filtering method

Su, X., Wang, S., PECHT, M. G., Ma, P., & Zhao, L. (2016) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331216642836 · Cited by 29 · see citing works

Prediction of remaining useful life under different conditions using accelerated life testing data

An, D., Choi, J.-H., & Kim, N. H. (2018) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-018-0507-z · Cited by 29 · see citing works

A Tutorial for Model-based Prognostics Algorithms based on Matlab Code

An, D., Choi, J.-H., & Kim, N. H. (2012) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2012.v4i1.2156 · Cited by 19 · see citing works

Prognostics 102 : Efficient Bayesian-Based Prognostics Algorithm in MATLAB

Kim, N.-H. (2018) · BiblioBoard Library Catalog (Open Research Library)

An Improved Particle Filter Method for Accurate Remaining Useful Life Prediction

Huang, D., Wang, M., Zhao, S., Wen, P., Chen, S., & Dou, Z. (2019) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2019.8819414

Performance-based probabilistic RUL prediction via particle filtering with neural-guided inference

Díaz-González, A., Coursey, A., Quiñones-Grueiro, M., Kulkarni, C. S., & Biswas, G. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.115026

Particle filter-based fatigue damage prognosis using prognostic-aided model updating

Li, T. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111244 · Cited by 28 · see citing works