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

A Critical Analysis of Crack Propagation Laws

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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

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

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

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

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

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 388

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

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

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning

Pugalenthi, K., Park, H., Hussain, S., & Raghavan, N. (2022) · Sensors · doi:10.3390/s22103803 · Cited by 30

A bearing prognosis framework based on deep wavelet extreme learning machine and particle filtering

Wang, L., Cao, H., & Fu, Y. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109763 · Cited by 28

Remaining Useful Life Prediction of Rolling Bearings Based on Segmented Relative Phase Space Warping and Particle Filter

Liu, H., Yuan, R. B., Lv, Y., Li, H., Gedikli, E. D., & Song, G. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3214623 · Cited by 28

Remaining Useful Life Prediction of Ball Screw Under Time-Varying Conditions With Limited Data

Yang, H., Mei, X., Jiang, G., Tao, T., Sun, Z., & Zhao, F. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3144351 · Cited by 27

Fault Prognosis Method for Solid Oxide Fuel Cells Based on Mechanism Degradation Process Model and Particle Filtering

Xu, Y., Jiang, C. S., Peng, J., Wu, X., Li, X. M., & Li, X. (2023) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2023.3246068 · Cited by 27

A holistic comparison of the different resampling algorithms for particle filter based prognosis using lithium ion batteries as a case study

Pugalenthi, K., & Raghavan, N. (2018) · Microelectronics Reliability · doi:10.1016/j.microrel.2018.08.007 · Cited by 27

Developing Prognostic Models Using Duality Principles for DC-to-DC Converters

Samie, M., Perinpanayagam, S., Alghassi, A., Motlagh, A. M. S., & Kapetanios, E. (2014) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2014.2376413 · Cited by 26

Remaining storage life prediction for an electromagnetic relay by a particle filtering-based method

Zhao, Y., Zio, E., & Fu, G. (2017) · Microelectronics Reliability · doi:10.1016/j.microrel.2017.03.026 · Cited by 26

Real-time process safety and systems decision-making toward safe and smart chemical manufacturing

Braniff, A., Akundi, S. S., Liu, Y., Dantas, B., Niknezhad, S. S., Khan, F., Pistikopoulos, E. N., & Tian, Y. (2025) · Digital Chemical Engineering · doi:10.1016/j.dche.2025.100227 · Cited by 30

RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques

Barbieri, M., Nguyen, K. T. P., Diversi, R., Medjaher, K., & Tilli, A. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01696-6 · Cited by 25

RMS Based Health Indicators for Remaining Useful Lifetime Estimation of Bearings

Klausen, A., van Khang, H., & Robbersmyr, K. G. (2022) · Modeling Identification and Control A Norwegian Research Bulletin · doi:10.4173/mic.2022.1.3 · Cited by 25

A hybrid prognostics approach for MEMS: From real measurements to remaining useful life estimation

Skima, H., Medjaher, K., Varnier, C., Dedu, E., & Bourgeois, J. (2016) · Microelectronics Reliability · doi:10.1016/j.microrel.2016.07.142 · Cited by 25

Prediction of fatigue crack propagation lives based on machine learning and data-driven approach

Sun, L., & Huang, X. (2022) · Journal of Ocean Engineering and Science · doi:10.1016/j.joes.2022.06.041 · Cited by 24

Remaining Useful Life Prognostics for the Electrohydraulic Servo Actuator Using Hellinger Distance-Based Particle Filter

Guo, R., & Sui, J. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2910919 · Cited by 24

Remaining useful life estimation under degradation and shock damage

Wang, H., Li, Y., Liu, Y., Yang, Y., & Huang, H. (2015) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x15573046 · Cited by 23

A Bayesian approach for a damage growth model using sporadically measured and heterogeneous on-site data from a steam turbine

Choi, W., Youn, B. D., Oh, H., & Kim, N. H. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.03.012 · Cited by 23

A New Hybrid Prognostic Methodology

Eker, O. F., Camci, F., & Jennions, I. K. (2019) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2019.v10i2.2727 · Cited by 22

Reliability modelling and assessment of a heterogeneously repaired system with partially relevant recurrence data

Peng, W., Balakrishnan, N., & Huang, H. (2018) · Applied Mathematical Modelling · doi:10.1016/j.apm.2018.02.020 · Cited by 21

Exponential Smoothing and Particle Filter

Pan, C., Chen, Y., Wang, L., & He, Z. (2019) · International Journal of Electrochemical Science · doi:10.20964/2019.10.15 · Cited by 21

An Age-Dependent and State-Dependent Adaptive Prognostic Approach for Hidden Nonlinear Degrading System

Pang, Z., Si, X., Hu, C., & Zhang, Z. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2021.1003859 · Cited by 21

An improved regularized particle filter for remaining useful life prediction in nuclear plant electric gate valves

Xu, R., Wang, H., Peng, M.-J., & Liu, Y.-K. (2021) · Nuclear Engineering and Technology · doi:10.1016/j.net.2021.12.001 · Cited by 21

Remaining Useful Life Estimation for Rolling Bearing With SIOS-Based Indicator and Particle Filtering

Qiu, M., Li, W., Jiang, F., & Zhu, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2831455 · Cited by 21

Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification

Arachchige, B., Perinpanayagam, S., & Jaras, R. (2017) · Applied Sciences · doi:10.3390/app7111172 · Cited by 20

Piecewise Model-Based Online Prognosis of Lithium-Ion Batteries Using Particle Filters

Pugalenthi, K., Park, H., & Raghavan, N. (2020) · IEEE Access · doi:10.1109/access.2020.3017810 · Cited by 19

System-level prognostics approach for failure prediction of reaction wheel motor in satellites

Park, H. J., Kim, S., Lee, J., Kim, N. H., & Choi, J.-H. (2022) · Advances in Space Research · doi:10.1016/j.asr.2022.11.028 · Cited by 19

Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Mitici, M., & de Pater, I. (2021) · Aerospace · doi:10.3390/aerospace8060168 · Cited by 18

A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier

Lopez-Salazar, C. A., Ekwaro-Osire, S., Dabetwar, S., & Alemayehu, F. M. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110517 · Cited by 18

Noise-dependent ranking of prognostics algorithms based on discrepancy without true damage information

Wang, Y., Gogu, C., Kim, N. H., Haftka, R. T., Binaud, N., & Bès, C. (2017) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.09.021 · Cited by 18

Inspection schedule for prognostics with uncertainty management

Kim, S., Choi, J.-H., & Kim, N. H. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108391 · Cited by 18

Estimation of Remaining Useful Life of a Fatigue Damaged Wind Turbine Blade with Particle Filters

Valeti, B., & Pakzad, S. N. (2018) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-319-74421-6_42 · Cited by 18

Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine

Khan, F., Eker, O. F., Khan, A. A., & Orfali, W. (2018) · Data · doi:10.3390/data3040049 · Cited by 18

A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals

Aunsri, N., Pipatphol, K., Thikeaw, B., Robroo, S., & Chamnongthai, K. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06768 · Cited by 17

Hybrid Particle Filter Trained Neural Network for Prognosis of Lithium-Ion Batteries

Pugalenthi, K., Park, H., Hussain, S., & Raghavan, N. (2021) · IEEE Access · doi:10.1109/access.2021.3116264 · Cited by 17

A signal segmentation and feature fusion based RUL prediction method for railway point system

Wang, F., Tang, T., Yin, J., Yuan-ping, L., & Ren, F. Y. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569703 · Cited by 17

Prognosis of gas turbine remaining useful life using particle filter approach

Ahsan, S., Lemma, ‪. A., & Muhammad, M. B. (2019) · Materialwissenschaft und Werkstofftechnik · doi:10.1002/mawe.201800219 · Cited by 17

Remaining useful life prediction of the ball screw system based on weighted Mahalanobis distance and an exponential model

Wen, J., & Gao, H. (2018) · Journal of Vibroengineering · doi:10.21595/jve.2018.19099 · Cited by 16

A DDoS Attack Detection Method Based on Natural Selection of Features and Models

Ma, R., Chen, X., & Zhai, R. (2023) · Electronics · doi:10.3390/electronics12041059 · Cited by 16

Wear Calculation-Based Degradation Analysis and Modeling for Remaining Useful Life Prediction of Ball Screw

Zhang, L., Gao, H., Dong, D., Fu, G., & Liu, Q. (2018) · Mathematical Problems in Engineering · doi:10.1155/2018/2969854 · Cited by 15

Non-Gaussian feature distribution forecasting based on ConvLSTM neural network and its application to robust machine condition prognosis

Szarek, D., Jabłoński, I., Zimroz, R., & Wyłomańska, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120588 · Cited by 16

Digital Twin-Enabled Health Prognostics for Smart Manufacturing Systems Under Uncertain Operating Conditions

Yang, H., Feng, C., Jiang, G., & Mei, X. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3441633 · Cited by 16

A dynamic maintenance decision approach based on maintenance action grouping for HVAC maintenance costs savings in Non-residential buildings

Nzukam, C., Voisin, A., Levrat, É., Sauter, D., & Iung, B. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.2551 · Cited by 15

Application of Kalman filter to Model-based Prognostics for Solenoid Valve

Tang, X., Xiao, M., & Hu, B. (2019) · Soft Computing · doi:10.1007/s00500-019-04311-w · Cited by 15

Dynamic business continuity assessment using condition monitoring data

Xing, J., Zeng, Z., & Zio, E. (2019) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2019.101334 · Cited by 15

Long-term reliability of the thermal performance of a flat-plate heat pipe using a prognostics method

Kim, H. J., Lee, S., Kong, Y. C., Jang, S. P., Choi, J. H., & Koo, J. (2014) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2014.11.051 · Cited by 14

Probabilistic method for fatigue crack growth prediction with hybrid prior

Wang, T., Bin, J., Renaud, G., Liao, M., Lu, G., & Liu, Z. (2022) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2021.106686 · Cited by 14

Remaining Useful Life Estimation of Wind Turbine Blades under Variable Wind Speed Conditions Using Particle Filters

Valeti, B., & Pakzad, S. N. (2018) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2018.v10i1.481 · Cited by 14

State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries

Wang, X., Song, Z., Yang, K., Yin, X., Geng, Y., & Wang, J. (2019) · Energies · doi:10.3390/en12010183 · Cited by 14

An enhanced copula-based method for data-driven prognostics considering insufficient training units

Xi, Z., & Zhao, X. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.03.015 · Cited by 14

Fatigue crack growth prediction model under variable amplitude loading conditions

Li, M., Liu, X., Li, Z., & Zhang, Y. (2021) · International Journal of Damage Mechanics · doi:10.1177/1056789521998737 · Cited by 13

A sliding sequence importance resample filtering method for rolling bearings remaining useful life prediction based on two Wiener-process models

Song, Y., Xu, S., & Lu, X. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acffe3 · Cited by 13

Phyformer: A degradation physics-informed self-data driven approach to machinery prognostics

Wang, Y., Li, M., Zheng, L., Shi, M., Zheng, Z., & Pei, X. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102772 · Cited by 13

Remaining Useful Life Prediction of Ball Screw Using Precision Indicator

Yang, H., Jiang, G., Sun, Z., Zhang, Z., Zhao, F., Tao, T., & Mei, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3087803 · Cited by 13

Early Prediction of the Health Conditions for Battery Cathodes Assisted by the Fusion of Feature Signal Analysis and Deep-Learning Techniques

Wang, W., Zhang, L., Yu, H., Yang, X., Zhang, T., Chen, S., Liang, F., Wang, H., Lu, X., Yang, S., et al. (2022) · Batteries · doi:10.3390/batteries8100151 · Cited by 13

Lifetime estimation of heat pipes in space applications using particle filtering, Arrhenius and FIDES methods

Kianfar, K., Joodaki, S., Dashti, I., & Asghari, S. (2021) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2021.100847 · Cited by 13

Reliability analysis of a multi-stack solid oxide fuel cell from a systems engineering perspective

Colombo, K. E., & Kharton, V. V. (2021) · Chemical Engineering Science · doi:10.1016/j.ces.2021.116571 · Cited by 13

Optimized Online Remaining Useful Life Prediction for Nuclear Circulating Water Pump Considering Time-Varying Degradation Mechanism

Liu, X., Cheng, W., Xing, J., Chen, X., Zhao, Z., Gao, L., Ding, B., Zhou, K., Zhi, Y., & Zhang, R. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3399878 · Cited by 12

Identifying new prognostic features for remaining useful life prediction using particle filtering and Neuro-Fuzzy System predictor

Boukra, T. (2015) · IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) · doi:10.1109/eeeic.2015.7165399 · Cited by 13

Prognostics for a Leaking Hydraulic Actuator Based on the F-Distribution Particle Filter

Guo, R., & Gan, Q. (2017) · IEEE Access · doi:10.1109/access.2017.2759119 · Cited by 12

Particle Filter Based Monitoring and Prediction of Spatiotemporal Corrosion Using Successive Measurements of Structural Responses

Yi, S., & Song, J. (2018) · Sensors · doi:10.3390/s18113909 · Cited by 12

Prognostic algorithms for L70 life prediction of solid state lighting

Padmasali, A. N., & Kini, S. G. (2015) · Lighting Research & Technology · doi:10.1177/1477153515579233 · Cited by 12

Application of Particle Filter to On-board life estimation of LED lights

Ruknudeen, F., & Asokan, S. (2017) · IEEE photonics journal · doi:10.1109/jphot.2017.2698409 · Cited by 12

Stochastic fractal search-optimized multi-support vector regression for remaining useful life prediction of bearings

Li, Y., Huang, X., Zhao, C., & Ding, P. (2021) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-021-03138-7 · Cited by 13

Long short-term memory neural network with scoring loss function for aero-engine remaining useful life estimation

Ren, L.-H., Ye, Z., & Zhao, Y.-P. (2022) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/09544100221103731 · Cited by 14

Prognosis of uncertain linear time-invariant discrete systems using unknown input interval observer

Robinson, E. I., Marzat, J., & Raïssi, T. (2019) · International Journal of Control · doi:10.1080/00207179.2019.1648854 · Cited by 12

A Model-Based Prognostics Framework to Predict Fatigue Damage Evolution and Reliability in Composites

Chiachío, J., Chiachío, M., Saxena, A., Rus, G., & Goebel, K. (2014) · PHM Society European Conference · doi:10.36001/phme.2014.v2i1.1551 · Cited by 12

Particle filter approach to lifetime prediction for microelectronic devices and systems with multiple failure mechanisms

Raghavan, N., & Frey, D. (2015) · Microelectronics Reliability · doi:10.1016/j.microrel.2015.06.089 · Cited by 11

BatteryTSFM: Generalizable long-horizon degradation prediction across conditions and chemistries via time series foundation models

Fan, Z., Guo, Y., Song, L., Du, J., Wang, H., & Li, X. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100646 · Cited by 10

A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples

Qin, H., Fan, X., Fan, Y., Wang, R., Shang, Q., & Zhang, D. (2023) · Applied Sciences · doi:10.3390/app13148498 · Cited by 10

A hybrid prognosis approach for life prediction of gears subjected to progressive pitting failure mode

Kundu, P., Kulkarni, M. S., & Darpe, A. K. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01852-6 · Cited by 10

Predicting Lumen Degradation of Light Emitting Diodes Using Hybrid Particle Filter Trained Neural Networks

Pugalenthi, K., Park, H., Hussain, S., & Raghavan, N. (2021) · IEEE Access · doi:10.1109/access.2021.3136266 · Cited by 10

Capacity Fast Prediction and Residual Useful Life Estimation of Valve Regulated Lead Acid Battery

He, Q., Zha, Y., Sun, Q., Pan, Z., & Liu, T. (2017) · Mathematical Problems in Engineering · doi:10.1155/2017/7835049 · Cited by 10

Prediction of Lithium-Ion Battery Capacity in UAVs

da Costa, E. F., de Souza, D. A., Pinto, V. P., Araújo, M. S., Peixoto, A. M., & da Costa, E. P. (2019) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit.2019.8820714 · Cited by 10

Application of particle filtering for prognostics with measurement uncertainty in nuclear power plants

Kim, G., Kim, H., Zio, E., & Heo, G. (2018) · Nuclear Engineering and Technology · doi:10.1016/j.net.2018.08.002 · Cited by 10

A novel ensemble fuzzy model for degradation prognostics of rolling element bearings

Jiang, Y. S., Zhu, H. J., Ding, C., & Pfeiffer, O. P. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-179277 · Cited by 9

Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries

El‐Dalahmeh, M., Al‐Greer, M., El-Dalahmeh, M., & Short, M. (2021) · IET Smart Grid · doi:10.1049/stg2.12013 · Cited by 10

Remaining useful life estimation for systems subject to multiple degradation mechanisms

Raghavan, N., & Frey, D. (2015) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2015.7245036 · Cited by 9

Real-Time Prognostics and Health Management Without Run-to-Failure Data on Railway Assets

Shimizu, M., Perinpanayagam, S., Namoano, B., & Starr, A. G. (2023) · IEEE Access · doi:10.1109/access.2023.3259221 · Cited by 8

An energy-based prognostic framework to predict evolution of damage in composite materials

Chiachío, M., Chiachío, J., Saxena, A., & Goebel, K. (2016) · Elsevier eBooks · doi:10.1016/b978-0-08-100148-6.00016-0 · Cited by 8

Degradation Data–Driven Analysis for Estimation of the Remaining Useful Life of a Motor

Banerjee, A., Gupta, S. K., & Putcha, C. (2021) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · doi:10.1061/ajrua6.0001114 · Cited by 8

Probabilistic approach for risk assessment of offshore hydrate wellbore during test production

Jiang, S., Chen, G., Zhu, Y. T., Liu, K., Chang, Y., & Wang, Z. (2022) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2022.05.044 · Cited by 8

Degradation analysis with nonlinear exponential‐dispersion process: Bayesian offline and online perspectives

Ding, Y., Zhu, R., Peng, W., & Xie, M. (2022) · Quality and Reliability Engineering International · doi:10.1002/qre.3179 · Cited by 8

Model-based prognosis algorithms with uncertainty propagation: Application to fatigue crack growth

Robinson, E. I., Marzat, J., & Raïssi, T. (2016) · Conference on Control and Fault-Tolerant Systems · doi:10.1109/systol.2016.7739792 · Cited by 7

A State-Space Backpropagation Algorithm for Nonlinear Estimation

Bjaili, H. A., Moinuddin, M., & Rushdi, A. M. A. (2019) · Circuits Systems and Signal Processing · doi:10.1007/s00034-019-01031-2 · Cited by 7

Particle Filter with Operational-Scalable Takagi–Sugeno Fuzzy Degradation Model for Filter-Clogging Prognosis

Sreenuch, T., Khan, F., & Li, J. (2015) · Journal of Aerospace Information Systems · doi:10.2514/1.i010385 · Cited by 7

Near-extreme system condition and near-extreme remaining useful time for a group of products

Wang, H., Li, Y., Huang, H., & Jin, T. (2017) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.01.023 · Cited by 7

A Review of Crack Propagation Modeling Using Peridynamics

Dias, J. P., Bazani, M. A., Paschoalini, A. T., & Barbanti, L. (2017) · Springer eBooks · doi:10.1007/978-3-319-55852-3_7 · Cited by 7

Prognostics 102: Efficient Bayesian-Based Prognostics Algorithm in MATLAB

Dong, T., An, D., & Kim, N. H. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.82781 · Cited by 7

A Hybrid TLBO-Particle Filter Algorithm Applied to Remaining Useful Life Prediction in the Presence of Multiple Degradation Factors

Rodrigues, L. R., Coelho, D. B. P., & Gomes, J. P. P. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185898 · Cited by 7

State of Health Estimation of Lithium-Ion Batteries Based on Combination of Gaussian Distribution Data and Least Squares Support Vector Machines Regression

Susilo, D. D., Widodo, A., Prahasto, T., & Nizam, M. (2018) · Materials science forum · doi:10.4028/www.scientific.net/msf.929.93 · Cited by 7

Constitutive modeling and reliability evaluation of interfacial slip damage in three-dimensional stacked micro/nano-scale through-silicon vias

Zhang, Y., Zheng, X., Ma, J., Wriggers, P., & Kong, P. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113669 · Cited by 6

Physics-Informed Neural Networks for RUL Prediction

Zhu, Q. X., Shi, Y., Feng, Y. W., & Wang, Y. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865662 · Cited by 6

Health indicator construction and remaining useful life prediction for space Stirling cryocooler

Song, L., Liang, H., Teng, W., & Guo, L. (2019) · Advances in Mechanical Engineering · doi:10.1177/1687814019896734 · Cited by 6

A General Data-Driven Framework for Remaining Useful Life Estimation with Uncertainty Quantification Using Split Conformal Prediction

Xu, W., & Zio, E. (2023) · International Conference on System Reliability and Safety (ICSRS) · doi:10.1109/icsrs59833.2023.10381131 · Cited by 6

Machine Learning Based Prognostics of Fatigue Crack Growth in Notch Pre-cracked Aluminum 7075-T6 Rivet Hole

Haynes, R., Joshi, G., & Bradley, N. (2018) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2018.v10i1.549 · Cited by 6

Real-time prognosis of random loaded structures via Bayesian filtering: A preliminary discussion

Corbetta, M., Sbarufatti, C., Manes, A., & Giglio, M. (2015) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2015.07.008 · Cited by 6

Physics-Based Prognostics

Kim, N., An, D., & Choi, J.-H. (2016) · Springer eBooks · doi:10.1007/978-3-319-44742-1_4 · Cited by 6

Particle Filter Based Approach for Wind Turbine High-Speed Shaft Bearing Health Prognosis

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Cyber–physical systems framework for AI in smart manufacturing and maintenance

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

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Prognostics 102 : Efficient Bayesian-Based Prognostics Algorithm in MATLAB

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An Improved Particle Filter Method for Accurate Remaining Useful Life Prediction

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