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

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

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

Particle Filter Based Integrated Health Monitoring in Bond Graph Framework

Jha, M., Dauphin-Tanguy, G., & Bouamama, B. O. (2017) · Springer eBooks · doi:10.1007/978-3-319-47434-2_7 · Cited by 5

Roughening Particle Filter Based Prognosis on Power MOSFETs Using ON-Resistance Variation

Pugalenthi, K., & Raghavan, N. (2018) · Prognostics and System Health Management Conference · doi:10.1109/phm-chongqing.2018.00206 · Cited by 5

Fundamentals of Prognostics

Bregón, A., & Daigle, M. J. (2019) · Springer eBooks · doi:10.1007/978-3-030-17728-7_17 · Cited by 5

Probabilistic Prognostics and Health Management: A Brief Summary

Alemayehu, F. M., & Ekwaro-Osire, S. (2017) · Springer eBooks · doi:10.1007/978-3-319-55852-3_1 · Cited by 5

Uncertainty quantification in prognostics: A data driven polynomial chaos approach

Duong, P. L. T., & Raghavan, N. (2017) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2017.7998318 · Cited by 5

Physics‐of‐Failure Approach to PHM

Cheng, S., Raghavan, N., Gu, J., Mathew, S., & PECHT, M. G. (2018) · Wiley eBooks · doi:10.1002/9781119515326.ch3 · Cited by 5

PHM of Light‐Emitting Diodes

Chang, M., Fan, J., Qian, C., & Sun, B. (2018) · doi:10.1002/9781119515326.ch14 · Cited by 5

Application of IoT in Industry 4.0 for Predictive Analytics

Banerjee, A., Datta, D., & Gupta, S. K. (2021) · doi:10.1002/9781119769323.ch10 · Cited by 5

Lithium-ion battery RUL prediction based on sequence decomposition and the PatchTST-MKAN model

Ma, J., Zhou, J., Wu, X., & Liu, T. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae31d1 · Cited by 5

From equation-centric modeling to transferable representation learning in battery lifetime prediction

Lu, J., Huang, X., (95310), R. C., Yu, Q., Xu, Y., & Li, J. (2026) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2026.05.054 · Cited by 4

Remaining useful life prognostic for degrading systems with age- and state-dependent jump-diffusion processes

Wen, B., Xiao, M., Tang, X., Ge, Y., Zhao, X., & Zhu, H. (2024) · ISA Transactions · doi:10.1016/j.isatra.2024.12.019 · Cited by 4

Incremental State-of-Charge determination of a Lithium-ion battery based on Capacity update using Particle Filtering framework

Chouhan, S., & Guha, A. (2023) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec55900.2023.10186967 · Cited by 4

DAE-Transformer-based Remaining Useful Life Prediction for Lithium-Ion Batteries in Energy Storages

Huang, B., Zeng, Z., Zhou, Y., Liu, J., Zheng, Q., Wang, L., Feng, Z., Sun, S., & Pan, Z. (2023) · International Conference on New Energy and Power Engineering (ICNEPE) · doi:10.1109/icnepe60694.2023.10429188 · Cited by 4

Probabilistic Fatigue Crack Growth Prediction for Pipelines with Initial Flaws

Choi, Y., & Lee, S.-J. (2024) · Buildings · doi:10.3390/buildings14061775 · Cited by 4

Prediction Model of Capacity Degradation in Lithium-Ion Batteries Based on Fatigue Damage Theory and Electrochemical Impedance Spectroscopy

Song, H., Xie, H., Zhang, Z., Zhang, Q., & Kang, Y. (2025) · Acta Mechanica Solida Sinica · doi:10.1007/s10338-025-00585-w · Cited by 4

Fault Diagnostics and Lifetime Prognostics for Phosphor-Converted White LED Packages

Fan, J., Qian, C., Fan, X., Zhang, G., & PECHT, M. G. (2017) · Solid state lighting technology and application series · doi:10.1007/978-3-319-58175-0_11 · Cited by 4

Applications of Artificial Neural Networks With Input and output Degradation data for Renewable Energy Systems Fault Prognosis

Hayder, A. N., & Saïdi, L. (2021) · International Renewable Energy Congress · doi:10.1109/irec52758.2021.9624894 · Cited by 4

Quantifying Uncertainty in Li-ion Battery Aging due to Unknown Usage with Hybrid Physics-Informed Neural Networks

do Nascimento, R. G., Viana, F. A. C., Corbetta, M., & Kulkarni, C. S. (2023) · AIAA SciTech Forum · doi:10.2514/6.2023-0536 · Cited by 4

Comprehensive Reliability Assessment Method for Lithium Battery Energy Storage Systems

Si, J., Tang, Y., Li, X., & Zhang, L. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2474/1/012009 · Cited by 4

Study on Partial Stratified Resampling for Particle Filter Based Prognosis on Li-Ion Batteries

Pugalenthi, K., & Raghavan, N. (2018) · Prognostics and System Health Management Conference · doi:10.1109/phm-chongqing.2018.00207 · Cited by 4

Hybrid residual fatigue life prediction approach for gear based on Paris law and particle filter with prior crack growth information

Liu, X., Jia, Y., He, Z., & Sun, L. (2017) · Journal of Vibroengineering · doi:10.21595/jve.2017.18327 · Cited by 4

A Particle-Filter-Based Online Method for Degradation Analysis with Exponential-Dispersion Process

Huang, R., & Peng, W. (2020) · Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM) · doi:10.1109/aparm49247.2020.9209330 · Cited by 4

Fault Diagnosis And Prognosis Of Satellite Attitude Control System With Reaction Wheels and Control Moment Gyros

Rahimi, A. (2021) · doi:10.32920/ryerson.14649306 · Cited by 4

Filtering and uncertainty propagation methods for model-based prognosis

Robinson, E. I. (2018) · doi:10.70675/a473f660z95a8z4868z94a3z38791c4e7c77 · Cited by 4

Kalman Filter-Based Systems Approach for Prognostics and Health Management of Electric Motors

Park, H. J., Lee, D., Kim, S., Kim, N. H., & Choi, J.-H. (2023) · Springer series in reliability engineering · doi:10.1007/978-3-031-28859-3_21 · Cited by 3

Remaining Useful Life Prediction of Li-Ion Battery Combining Model-Based Method with Data-Driven Algorithms

Jiao, Z., Fan, X., Zheng, Y., & Zhang, X. (2019) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2019.10106 · Cited by 3

Remaining useful life prognosis for wind turbine using a neural network with a long-term prediction

Saïdi, L., Hayder, A. N., & Saidi, M. (2022) · Wind Engineering · doi:10.1177/0309524x221085133 · Cited by 3

A Trade-Off Analysis between Sensor Quality and Data Intervals for Prognostics Performance

Park, H. J., Kim, N. H., & Choi, J.-H. (2022) · Sensors · doi:10.3390/s22197220 · Cited by 3

Particle Filter Based Approach for Remaining Useful Life Prediction of High-Speed Shaft Bearing in Wind Turbine Generators

Kramti, S. E., Ali, J. B., André, H., Brhhoefer, E., & Sayadi, M. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.100043 · Cited by 3

Performance Prognostics of Gas Turbines Using Nonlinear Filter

Ahsan, S., Lemma, ‪. A., Hashmi, M. B., & Gebremariam, M. A. (2022) · Lecture notes in mechanical engineering · doi:10.1007/978-981-19-1939-8_38 · Cited by 3

Wiener Process and Extreme Learning Machine Swarm Based Transfer Learning for Remaining Useful Life Prediction of Lithium Battery

Chen, X., & Liu, Z. (2023) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi59194.2023.10270463 · Cited by 3

Particle Filter Prognostic Applied in Landing Gear Retraction

Cerdeira, P. B. O., Galvão, R. K. H., & Malere, J. P. (2013) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2013.v5i1.2180 · Cited by 3

Fault Diagnosis And Prognosis Of Satellite Attitude Control System With Reaction Wheels and Control Moment Gyros

Rahimi, A. (2021) · doi:10.32920/ryerson.14649306.v1 · Cited by 3

A case study on health prediction of an industrial diesel motor using particle filtering

Vu, H. C., Do, P., Iung, B., & Peysson, F. (2017) · Prognostics and System Health Management Conference · doi:10.1109/phm.2017.8079245 · Cited by 2

Fault Prognostics of Rolling Bearings Using a Hybrid Approach

Camargos, M. O., de Bessa, I. V., D’Ângelo, M. F. S. V., & Palhares, R. M. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.12.2435 · Cited by 2

Prediction-Interval-Based Credibility Criteria of Prognostics Results for Practical Use

An, D. (2022) · Processes · doi:10.3390/pr10030473 · Cited by 2

Application of particle filter variants to estimate the remaining useful life

Banerjee, A., Bajpai, M., & Putcha, C. (2023) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2023.103531 · Cited by 2

An online hybrid prognostics ANFIS-PF method with an application to gearbox for RUL prediction

Govahianjahromi, A., Kim, J., Hamadache, M., & Lee, D. (2017) · PHM Society Asia-Pacific Conference · doi:10.36001/phmap.2017.v1i1.1797 · Cited by 2

A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model

An, D. (2024) · Applied Sciences · doi:10.3390/app15010266 · Cited by 2

Hedge asset for stock markets: Cryptocurrency, Cryptocurrency Volatility Index (CVI) or Commodity

Bhuiyan, R. A., Mukherjee, T. C., Tarique, K. M., & Zhang, C. (2025) · Quantitative Finance and Economics · doi:10.3934/qfe.2025005 · Cited by 2

Fault Prognostics of Micro-Electro-Mechanical Systems Using Particle Filtering**This work has been supported by the Région Franche-Comté and the ACTION Labex project (contract ANR-11-LABX-0001-01)

Skima, H., Medjaher, K., Varnier, C., Dedu, E., Bourgeois, J., & Noureddine Zerhouni, A. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.11.039 · Cited by 2

Probabilistic Maintenance-Free Operating Period via Bayesian Filter with Markov Chain Monte Carlo (MCMC) Simulations and Subset Simulation

Shiao, M., Chen, T.-K., & Mao, Z. (2018) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-319-74793-4_27 · Cited by 2

Residual Life Prediction of Satellite Lithium-Ion Batteries in Orbital Environment

Bin, Y., Yao, L. Y., & Zhang, T. (2018) · International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) · doi:10.1109/sdpc.2018.8664877 · Cited by 2

Remaining useful life prediction of silicone foam using matching pursuit and a particle filter

Wang, J., Sheng, J., Zhang, S., & Jiang, J. (2022) · Mechanics of Time-Dependent Materials · doi:10.1007/s11043-022-09562-z · Cited by 2

Degradation Detection and RUL Prediction of Rolling Bearings Based on Gini Index and Particle Filter

Wen, H., Zhang, L., & Sinha, J. K. (2024) · Mechanisms and machine science · doi:10.1007/978-3-031-49421-5_78 · Cited by 2

Adaptive Noise Parameter Determination Based on a Particle Filter Algorithm

Cho, H.-T., & Mun, S. (2016) · Mathematical Problems in Engineering · doi:10.1155/2016/3570509 · Cited by 2

Doppler Frequency Estimation for a Maneuvering Target Being Tracked by Passive Radar Using Particle Filter

Almanofi, A. M., Malki, A., & Kazem, A. (2020) · Journal of Communications Software and Systems · doi:10.24138/jcomss.v16i4.1097 · Cited by 2

Particle Filter Based Prognostic Approach for Automotive Motor

Banerjee, A., Putcha, C., & Gupta, S. K. (2021) · World Symposium on Artificial Intelligence (WSAI) · doi:10.1109/wsai51899.2021.9486338 · Cited by 2

Renewable Energy Systems Prognostics and Health Management: A Review of Recent Advances

Saïdi, L., & Benbouzid, M. (2021) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon48115.2021.9589308 · Cited by 2

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

Kan, M. S. (2017) · Queensland University of Technology · doi:10.5204/thesis.eprints.110621 · Cited by 2

A Particle Filtering Based Approach for Transformer Winding Degradation Prognostics

Guo, H., Xu, A., Wang, K., Han, X., Liu, Y., Zhang, X., & Lv, D. (2018) · Prognostics and System Health Management Conference · doi:10.1109/phm-chongqing.2018.00125 · Cited by 1

Sustainable Aviation: A Review in Intelligent Health Management for Decarbonized Aerospace Propulsion

Xiao, Y., Zhang, J., Ji, B., & Ruiz, H. S. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-3940-0_45 · Cited by 1

Probabilistic Model-Based Prognostics Using Meshfree Modeling

Ekwaro-Osire, S., Endeshaw, H. B., Alemayehu, F. M., & Gecgel, O. (2017) · Springer eBooks · doi:10.1007/978-3-319-55852-3_5 · Cited by 1

Prediction of Remaining Engine Life Based on Multi-sensing Fusion

Yafei, S., Zheng, H., Duan, S., & Dongzhu, Z. (2020) · International Conference on Mechanical and Aerospace Engineering · doi:10.1109/icmae50897.2020.9178857 · Cited by 1

A Comparison Study of Machine Learning Enabled Filtering Methods for Battery Management

Kohtz, S., & Wang, P. (2020) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm49022.2020.9187029 · Cited by 1

RUL Prediction For Turbine Disc Based On High-Order Particle Filtering

Fu, Y., & Cao, H. (2020) · International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) · doi:10.1109/icsmd50554.2020.9261721 · Cited by 1

Comparative Study of Data-Driven Models in Motor RUL Estimation

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/ajrua6.0001186 · Cited by 1

Smart Electricity Meter Prognostics Based on Lithium Battery RUL Prediction

Chen, Y., Chen, Z., Zhu, M., Liao, Y., Luo, F., & Li, X. (2021) · Distributed Generation & Alternative Energy Journal · doi:10.13052/dgaej2156-3306.3733 · Cited by 1

Bayesian inference-based prognosis of fatigue damage for MPPO polymer using Zhurkov fatigue life model

Doh, J. (2022) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x221132870 · Cited by 1

A Fast Monte Carlo Method for Model-based Prognostics Based on Stochastic Calculus

Corbetta, M., & Kulkarni, C. S. (2019) · AIAA SciTech Forum · doi:10.2514/6.2019-2232 · Cited by 1

Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis

Ossai, C. I. (2020) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2018.v9i2.2703 · Cited by 1

Methods of Identifying Correlated Model Parameters with Noise in Prognostics

Dong, T., & Kim, N. H. (2021) · Aerospace · doi:10.3390/aerospace8050129 · Cited by 1

Comparative Study of Health Monitoring Sensors based on Prognostic Performance

Park, H. J., Kim, N. H., & Choi, J.-H. (2022) · PHM Society European Conference · doi:10.36001/phme.2022.v7i1.3350 · Cited by 1

Data-Model Fusion RUL Prediction

Lei, Y., Li, N., & Li, X. (2022) · Springer eBooks · doi:10.1007/978-981-16-9131-7_6 · Cited by 1

Particle Filter and Its Variants for Degradation State Estimation and Remaining Useful Life Prediction

Gu, H., Mahmoud, H., Arun, R. L., Liu, J., & Ma, X. (2023) · Prognostics and Health Management Conference (PHM) · doi:10.1109/phm58589.2023.00055 · Cited by 1

An Intelligent PHM System for Bearing Fault Phase Diagnosis and Multi-Phase RUL Prediction using Oil Debris Monitors

Raj, L. (2021) · doi:10.22215/etd/2021-14694 · Cited by 1

Physics-Informed Diagnostic and Prognostic Models for Rolling Element Bearings Using Oil Debris Data

Gu, H. (2023) · doi:10.22215/etd/2023-15776 · Cited by 1

A Study of Online Inspection for Multi-level Prognostics

Zhang, D., Cadet, C., Steiner, N. Y., & Bérenguer, C. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.2549

Health monitoring of feedback controlled mechatronic systems

Al-Khafaji, A. A. G. (2017)

A comparative study of particle filters for prognostics implementation in industry

Do, P. (2018) · PHM Society European Conference · doi:10.36001/phme.2018.v4i1.457

Sustainable Manufacturing of High-Precision, Heat-Resistant Aspherical Lenses Using Ultraviolet Illumination With Prognosis of Remaining Useful Life

Kim, J., Lim, J., Park, C., Myung, H., Lee, J., & Kang, S. (2018) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4042125

An Investigation on the Long-term Reliability Evaluation of Swash Plate Type Hydraulic Pump using Particle Filter Method

Kim, H. J., & 박경식 (2018) · Journal of the Korean Society of Mechanical Technology · doi:10.17958/ksmt.20.5.201810.618

Software Framework for Tribotronic SystemsPreprint

Kansanaho, J., & Kärkkäinen, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.13764

An Unscented Rao-Blackwellised Particle Filter for Prognostics and Remaining Useful Life Estimation of Battery

Yuvapoositanon, P. (2019) · International Electrical Engineering Congress (iEECON) · doi:10.1109/ieecon45304.2019.8938839

Tool Wear Conditions Monitoring Based on Kernel Smoothing Density and Deep LearningPreprint

Bazi, R., Benkedjouh, T., Habbouche, H., & Rechak, S. (2021) · Research Square · doi:10.21203/rs.3.rs-254518/v1

Prediction of lithium-ion battery capacity by functional monitoring data using functional principal component analysis

Ullah, M. S. (2020) · doi:10.32469/10355/83928

A hybrid prognostic approach for battery health monitoring and remaining-useful-life prediction

Ahwiadi, M. (2021)

Bibliography

Gouriveau, R., Medjaher, K., & Noureddine Zerhouni, A. (2016) · doi:10.1002/9781119371052.biblio

Data-Driven and Physics Model-Based Structural Prognosis

Mao, Z. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-822944-6.00083-9

Model-based health state estimation method for proton exchange membrane fuel cells

Peng, Z., Zhou, S., & Sun, Q. (2023) · Advances in Computer Signals and Systems · doi:10.23977/acss.2023.070306

Research on the Life Prediction of Battery in 500kV Substation

Xiao, Y., Liao, Y., Wu, M., Xiong, W., Ma, Y., & Luo, L. (2023) · Panda Forum on Power and Energy (PandaFPE) · doi:10.1109/pandafpe57779.2023.10140225

Remaining Useful Life Prediction for Anti-friction Bearings Based on Envelope Spectrum and Extended Kalman Filter

Wen, H., Zhang, L., & Sinha, J. K. (2023) · Mechanisms and machine science · doi:10.1007/978-3-031-40459-7_26

A Ddos Attack Detection Framework: Leveraging Feature Selection Integration and Random Forest Optimization for Improved SecurityPreprint

Chong, Y.-W., Ali, T. E., Manickam, S., Yusoff, M. N., Yau, K. A., & Keoh, S. L. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4651305

An All-Movable Horizontal Tail Numerical Model for Free-play Prognostics

Scott, M., Candon, M. J., Hale, E., Muscarello, V., Delgado‐Gutiérrez, A., Verhagen, W. J. C., Levinski, O., & Marzocca, P. (2024) · AIAA SciTech Forum · doi:10.2514/6.2024-2440

Battery prognostics based on discharge voltage drop for energy storage applications

Kim, D., Lee, J., Kim, S. G., Choi, J.-H., Song, H. S., & Park, S. H. (2017) · PHM Society Asia-Pacific Conference · doi:10.36001/phmap.2017.v1i1.1852

Prediction Model of Capacity Degradation in Lithium-Ion Batteries Based on Electrochemical Impedance SpectroscopyPreprint

Song, H., Xie, H., Zhang, Z., Zhang, Q., & Kang, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4775283

Lifetime Prediction Based on Age-State Dependent Diffusion Model

Wen, B., Deng, S., Li, C., & Zhu, H. (2023) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-hangzhou58797.2023.10482706

Improved particle filter for fatigue crack propagation prediction using SH0 wave online monitoring

Zhang, Y., Jia, J., Wang, M., Gu, Y., & Tu, S. T. (2025) · Insight - Non-Destructive Testing and Condition Monitoring · doi:10.1784/insi.2025.67.9.539

Intelligent Diagnosis and Prognosis of Mechanical Systems with Big Data Era

Lei, Y., Li, N., & Yang, B. (2025) · Lecture notes in control and information sciences · doi:10.1007/978-981-96-9033-6_14

An improved similarity-based prognostic approach for estimating remaining useful life using gated recurrent units, migratory linear regression, and greedy matching strategy

Li, G. H. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae5446

Drift detection and characterization for supervision, diagnosis and prognosis of dynamical systems

Chammas, A. (2014) · doi:10.70675/4d21918ezc0e0z486fz871cza2287baa456a

Business continuity of energy systems : a quantitative framework for dynamic assessment and optimization

Xing, J. (2019) · doi:10.70675/2d1b8ff5zc4d3z41c3z96aaz6f99ef06003e

Deterministic and non-deterministic simulation coupling for predictive maintenance

Schwartz, S. (2020) · doi:10.70675/aea49c8fzcc62z49c8z9f3ez93ded352a97e

Prognostic Evaluation of Stochastic Models with the Consideration of Sensor Degradation

Hachem, H. (2022) · doi:10.70675/4a7caf74z4bccz4cc7zbf4cz9f0416e5a819

Real-Time Reliability Assessment of Multivariable Systems With Multiple Failure Modes

Wang, J., Ma, X., & zhang, Y. (2026) · IEEE Transactions on Reliability · doi:10.1109/tr.2026.3686630

Seismic response prediction of wind turbine towers using a Bayesian framework based on chaotic map-based particle swarm optimized particle filter

Ren, Q., Feng, X., Kou, Y., Chen, Q., & Xu, Y. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113045

A Context-Aware LLM Framework for Battery State-of-Charge Prediction

Ren, X., Yang, C., Zhou, Y., Tu, J., Li, C., & Li, Q. (2026) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/icaisisas68969.2026.11567768

A Hybrid Genetic Algorithm–Particle Filter for Fatigue Crack Propagation Prediction

Mei, L., Wu, X., Liu, Y., Wang, J., & Ma, B. (2026) · Applied Sciences · doi:10.3390/app16168327

REAL-TIME PROGNOSTICS AND HEALTH MANAGEMENT WITHOUT RUN-TO-FAILURE DATA ON RAILWAY ASSETS

Prasad, M. S., & Kumari, M. T. S. (2024) · Journal of Science and Technology · doi:10.46243/jst.2024.v9.i012.pp01-15

Physics-informed prognostic model for rolling element bearing using auxiliary particle filter with resample-move and inline oil debris data

Gu, H., & Liu, J. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/aea4da

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

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

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

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