Journal Publications
Sánchez-Rivero, M.; Duarte-Mermoud, M.; Travieso-Torres, J.; Orchard, M.; Ceballos-Benavides, G., “Analysis of Fractional Order-Adaptive Systems Represented by Error Model 1 Using a Fractional-Order Gradient Approach,” Mathematics, 12(20), 3212, 2024. DOI: https://doi.org/10.3390/math12203212
Ceballos, G.; Duarte-Mermoud, M.; Orchard, M.; Ehijo, A., “Enhancing the Pitch-Rate Control Performance of an F-16 Aircraft Using Fractional-Order Direct-MRAC Adaptive Control,” Fractal and Fractional, 8, 338, 2024. DOI: https://doi.org/10.3390/fractalfract8060338.
Gutierrez, J.M.; Astroza, R.; Jaramillo, F.; Orchard, M.; Guarini, M., “Evolution of modal parameters of composite wind turbine blades under short- and long-term forced vibration tests,” Journal of Civil Structural Health Monitoring, 2024. DOI: https://doi.org/10.1007/s13349-024-00773-1
Muxica, D.; Rivera, S.; Orchard, M.; Ahumada, C.; Jaramillo, F.; Bravo, F.; Gutierrez, J.M.; Astroza, R., “Autonomous Sensor System for Wind Turbine Blade Vibration Measurement and Structural Health Monitoring,” Sensors, 24(6), 1733, 2024. DOI: https://doi.org/10.3390/s24061733
Vicuña, M.; Silva, J.; Mendez, R.; Orchard, M.; Espinosa, S.; Tregloan-Reed, J., “Optimal photometry of point sources: Joint source flux and background determination on array detectors – from theory to practical implementation,“ Publications of the Astronomical Society of the Pacific, Vol. 136, 014501, 2024. DOI: https://doi.org/10.1088/1538-3873/ad0ca3
Jara, C.; Orchard, M.; Devia, C., “Exploring the Benefits of Images with Frequency Visual Content in Predicting Human Ocular Scanpaths using Artificial Neural Networks,” Expert Systems With Applications, Vol. 239, 121839, 2024. DOI: https://doi.org/10.1016/j.eswa.2023.121839
Martinez-Gomez, M.; Orchard, M.; Bozhko, S., “Dynamic Average Consensus with Anti-windup applied to Interlinking Converters in AC/DC Microgrids under Economic Dispatch and Delays,” IEEE Transactions on Smart Grid, Vol. 14, Issue 5, pp. 4137-4140, 2023. DOI: https://doi.org/10.1109/TSG.2023.3291208
Peña-Ancavil, E.; Estevez, C.; Sanhueza, A.; Orchard, M., “Adaptive Scalable Video Streaming (ASViS): An Advanced ABR Transmission Protocol for Optimal Video Quality,” Electronics, Vol. 12, Issue 21, 4542, 2023. DOI: https://doi.org/10.3390/electronics12214542
Kordestani, M.; Mousavi, M.; Chaibakhsh, A.; Orchard, M.; Khorasani; K.; Saif, M., “A New Compressor Failure Prognostic Method Using Nonlinear Observers and a Bayesian Algorithm for Heavy-Duty Gas Turbines,” IEEE Sensors Journal, Vol. 23, no. 4, pp. 3889-3900, 2023. DOI: https://doi.org/10.1109/JSEN.2022.3233585
Videla, M.; Mendez, R.; Silva, J.; Orchard, M., “Optimal observational scheduling framework for binary and multiple stellar systems,” Publications of the Astronomical Society of the Pacific, Vol. 135, 014501, pp. 1-19, 2023. DOI: https://doi.org/10.1088/1538-3873/acaebc
Acuña, D.; Orchard, M., “Near-Instantaneous Battery End-of-Discharge Prognosis via Uncertain Event Likelihood Functions,” ISA Transactions, Vol.135, pp. 199-212, 2023. DOI: https://doi.org/10.1016/j.isatra.2022.09.040
Torres, J.; Orchard, M.; Torres-Torriti, M.; Auat, F., “GNSS-based estimation of average instantaneous power consumption in electric vehicles,” IEEE Transactions on Industrial Electronics, Vol. 70, no. 9, pp. 9281-9290, 2023. DOI: https://doi.org/10.1109/TIE.2022.3206748
Tamssaouet, F.; Nguyen, K.; Medjaher, K.; Orchard, M.; “System-level failure prognostics: Literature review and main challenges,” Journal of Risk and Reliability, Vol. 237, Issue 3, pp. 524-545, 2023. DOI: https://doi.org/10.1177/1748006X221118448
Alvarado, D.; Moreno, D.; Orchard, M.; Kirschen, D., “Cost-benefit Analysis of Maintenance Plans: Case Study of the Power System of a Large Industrial Facility,” IEEE Transactions on Power Systems, Vol. 38, Issue 3, pp. 2046-2057, 2023. DOI: https://doi.org/10.1109/TPWRS.2022.3185376
Ceballos, G.; Duarte-Mermoud, M.; Orchard, M.; Travieso-Torres, J.C., “Pitch Angle Control of an Airplane using Fractional Order Direct Model Reference Adaptive Controllers,” Fractal and Fractional, 7, 342, 2023. DOI: https://doi.org/10.3390/fractalfract7040342
Futalef, J.P.; Muñoz-Carpintero, D.; Rozas, H.; Orchard, M., “An online decision-making strategy for routing of electric vehicle fleets,” Information Sciences, Vol. 625, pp. 715–737, 2023. DOI: https://doi.org/10.1016/j.ins.2022.12.108
Kordestani, M.; Orchard, M.; Khorasani; K.; Saif, M., “An Overview of the State-of-the-Art in Aircraft Prognostic and Health Management Strategies,” IEEE Transactions on Instrumentation & Measurement, Vol. 72, pp. 1-15, Art no. 3505215, 2023. DOI: https://doi.org/10.1109/TIM.2023.3236342
Arias-Cazco, D.; Rozas, H.; Jimenez, D.; Orchard, M.; Estevez, C., “Unifying Criteria for Calculating the Levelized Cost of Driving in Electro-Mobility Applications,” World Electric Vehicle Journal, 13(7), 19, 2022. DOI: https://doi.org/10.3390/wevj13070119
Yaqoob, M.; Lashab, A.; Vasquez, J.; Guerrero, J.; Orchard, M.; Bintoudi, A., “A Comprehensive Review on Small Satellite Electrical Power System,” IEEE Transactions on Power Electronics, Vol. 37, Issue 10, pp. 12741-12762, 2022. DOI: https://doi.org/10.1109/TPEL.2022.3175093
Kordestani, M.; Rezamand, M.; Orchard, M., Carriveau, R.; Ting, D.; Rueda, L.; Saif, M., “New Condition-based Monitoring and Fusion Approaches with a Bounded Uncertainty for Bearing Lifetime Prediction,” IEEE Sensors Journal, Vol. 22, Issue 9, pp. 9078-9086, 2022. DOI: https://doi.org/10.1109/JSEN.2022.3159624
Videla, M.; Méndez, R.; Claveria, R.; Silva, J.; Orchard, M., “Bayesian inference in single-line spectroscopic binaries with a visual orbit,” The Astronomical Journal, Vol. 163, No. 5, pp. 1-29, 2022. DOI: https://doi.org/10.3847/1538-3881/ac5ab4
Jaramillo, F.; Gutiérrez, J.; Orchard, M.; Guarini, M.; Astroza, R., “A Bayesian approach for fatigue damage diagnosis and prognosis of wind turbine blades,” Mechanical Systems and Signal Processing, Vol. 174, 109067 (pp. 1-18), 2022. DOI: https://doi.org/10.1016/j.ymssp.2022.109067
Travieso-Torres, J.; Contreras, C.; Hernández, F.; Duarte-Mermoud, M.; Aguila-Camacho, N.; Orchard, M., “Adaptive Passivity-based Control Extended for Unknown Control Direction,” ISA Transactions, Vol. 122, pp. 398-408, 2022. DOI: https://doi.org/10.1016/j.isatra.2021.04.028
González, M.; Silva, J.; Videla, M.; Orchard, M., “Data-Driven Representations for Testing Independence: Modeling, Analysis and Connection with Mutual Information Estimation,” IEEE Transactions on Signal Processing, Vol. 70, pp. 158-173, 2022. DOI: https://doi.org/10.1109/TSP.2021.3135689
Tamssaouet, F.; Nguyen, K.; Medjaher, K.; Orchard, M., “Fresh New Look on System-level Prognostic: Handling Multi-component Interactions, Mission Profile Impacts, and Uncertainty Quantification,” International Journal of Prognostics and Health Management, Vol. 12, Issue 2, 2021. DOI: https://doi.org/10.36001/IJPHM.2021.v12i2.2777
Martinez-Gomez, M.; Navas, A.; Orchard, M.; Bozhko, S.; Burgos-Mellado, C.; Cardenas, R., “Multi-Objective Finite-Time Control for the Interlinking Converter on Hybrid AC/DC Microgrids,” IEEE Access, Vol. 9, pp. 116183 – 116193, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3105649
Jaras, I.; Harada, T.; Orchard, M.; Maldonado, P.; Vergara, R., “Extending the integrate-and-fire model to account for metabolic dependencies,” European Journal of Neuroscience, Vol. 54, Issue 4, pp. 5249-5260, 2021. DOI: https://doi.org/10.1111/ejn.15326
Rozas, H.; Muñoz-Carpintero, D.; Sáez. D.; Orchard, M., “Solving in Real-time the Dynamic and Stochastic Shortest Path Problem for Electric Vehicles by a Prognostic Decision Making Strategy,” Expert Systems with Applications, Vol. 184, 115489, 2021. DOI: https://doi.org/10.1016/j.eswa.2021.115489
Villegas, C.; Méndez, R.; Silva, J.; Orchard, M., “Bayes-based orbital elements estimation in triple hierarchical stellar systems,” Publications of the Astronomical Society of the Pacific, Vol. 133, No. 1025, 074501, 2021. DOI: https://doi.org/10.1088/1538-3873/ac0239
Kordestani, M.; Saif, M.; Orchard, M.; Razavi-Far, R.; Khorasani, K., “Failure Prognosis with Some Applications – A Survey of Recent Literature,” IEEE Transactions on Reliability, Vol. 70, Issue 2, pp. 728-748, 2021. DOI: https://doi.org/10.1109/TR.2019.2930195
Tamssaouet, F.; Nguyen, K.; Medjaher, K.; Orchard, M., “Degradation Modeling and Uncertainty Quantification for System-Level Prognostics,” IEEE Systems Journal, Vol. 15, Issue 2, pp. 1628-1639, 2021. DOI: https://doi.org/10.1109/JSYST.2020.2983376
Tamssaouet, F.; Nguyen, K.; Medjaher, K.; Orchard, M., “Online joint estimation and prediction for system-level prognostics under component interactions and mission profile effects,” ISA Transactions, Vol. 113, pp. 52-63, 2021. DOI: https://doi.org/10.1016/j.isatra.2020.05.002
Rozas, H.; Troncoso-Kurtovic, D.; Ley, C.; Orchard, M., “Lithium-Ion Battery State-of-Latent-Energy (SoLE): A Fresh New Look to the Problem of Energy Autonomy Prognostics in Storage Systems,” Journal of Energy Storage, Vol. 40, 102735, 2021. DOI: https://doi.org/10.1016/j.est.2021.102735
Ley, C.; Orchard, M., “Simultaneous Inference of Lithium-Ion Battery Polarising Impedance Surface and Capacity Degradation using a Hybrid Neural Adaptive State Space Model,” Journal of Energy Storage, Vol. 36, 102370, 2021. DOI: https://doi.org/10.1016/j.est.2021.102370
Rezamand, M.; Kordestani, M.; Carriveau, R.; Ting, D.; Orchard, M.; Saif, M., “Improved Remaining Useful Life Estimation of Wind Turbine Drivetrain Bearings Under Varying Operating Conditions (VOC),” IEEE Transactions on Industrial Informatics, Vol. 17, Issue 3, pp. 1742-1752, 2021. DOI: https://doi.org/10.1109/TII.2020.2993074
Acuña, D.; Orchard, M.; Wheeler, P., “Computation of Time Probability Distributions for the Occurrence of Uncertain Future Events,” Mechanical Systems and Signal Processing, Vol. 150, 107332, 2021. DOI: https://doi.org/10.1016/j.ymssp.2020.107332
Rezamand, M.; Kordestani, M.; Carriveau, R.; Ting, D.; Orchard, M.; Saif, M., “Critical Wind Turbine Components Prognostics: A Comprehensive Review,” IEEE Transactions on Instrumentation & Measurement, Vol. 69, Issue 12, pp. 9306-9328, 2020. DOI: https://doi.org/10.1109/TIM.2020.3030165
Paccha-Herrera. E.; Calderón-Muñoz, W.; Orchard, M.; Jaramillo, F.; Medjaher, K., “Thermal modeling approaches for a LiCoO2 lithium-ion battery. A comparative study with experimental validation,” Batteries, 6(3), 40, 2020. DOI: https://dx.doi.org/10.3390/batteries6030040
Díaz, C.; Quintero, V.; Pérez, A.; Jaramillo, F.; Burgos-Mellado, C.; Rozas, H.; Orchard, M.; Sáez, D.; Cárdenas, R., “Particle-filtering-based Prognostics for the State of Maximum Power Available in Lithium-Ion Batteries at Electromobility Applications” IEEE Transactions on Vehicular Technology, Vol. 69, Issue 7, pp. 7187-7200, 2020. DOI: https://doi.org/10.1109/TVT.2020.2993949
Rozas, H.; Jaramillo, F.; Pérez, A.; Jimenez, D.; Orchard, M.; Medjaher, K., “A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms,” Mechanical Systems and Signal Processing, Vol. 135, 106421, 2020. DOI: https://doi.org/10.1016/j.ymssp.2019.106421
Aguila-Camacho, N.; Duarte-Mermoud, M.; Orchard, M., “Fractional order controllers for throughput and product quality control in a grinding mill circuit,” European Journal of Control, Vol. 51, pp. 122-134, 2020. DOI: https://doi.org/10.1016/j.ejcon.2019.08.002
Orchard, M.; Muñoz-Poblete, C.; Huircan, JI.; Galeas, P.; Rozas, H., “Harvest Stage Recognition and Potential Fruit Damage Indicator for Berries based on Hidden Markov Models and the Viterbi Algorithm,” Sensors, 19(20), 4421, 2019. DOI: https://doi.org/10.3390/s19204421
Clavería, R.; Méndez, R.; Silva, J.; Orchard, M., “Visual binary stars with partially missing data: Introducing multiple imputation in astrometric analysis,” Publications of the Astronomical Society of the Pacific, 131:084502, no. 1002, pp. 1-19, 2019. DOI: https://doi.org/10.1088/1538-3873/ab22e2
Quintero, V.; Estevez, C.; Orchard, M.; and Pérez, A., “Improvements of Energy-Efficient Techniques in WSNs: A MAC-Protocol Approach,” IEEE Communications Surveys and Tutorials, vol. 21, Issue 2, pp. 1188-1208, 2019. DOI: https://doi.org/10.1109/COMST.2018.2875810
Pizarro-Carmona, V.; Cortés-Carmona, M.; Palma-Behnke, R.; Calderón-Muñoz, W.; Orchard, M.; Estévez, P., “An Optimized Impedance Model for the Estimation of the State-of-Charge of a Li-Ion Cell: The Case of a LiFePO4 (ANR26650),” Energies , 12(4), 681, 2019. DOI: https://doi.org/10.3390/en12040681
Kordestani, M.; Zanj, A.; Orchard, M.; and Saif, M., “A Modular Fault Diagnosis and Prognosis Method for Hydro-control Valve System based on Redundancy in Multi-Sensor Data Information,” IEEE Transactions on Reliability, vol. 68, Issue 1, pp. 330-341, 2019. DOI: https://doi.org/10.1109/TR.2018.2864706
Sierra, G.; Orchard, M.; Goebel, K.; Kulkarni, C., “Battery Health Management for Small-size Rotary-wing Electric Unmanned Aerial Vehicles: An Efficient Approach for Constrained Computing Platforms,” Reliability Engineering and System Safety, vol. 182, pp. 166-178, 2019. DOI: https://doi.org/10.1016/j.ress.2018.04.030
Quintero, V.; Perez, A.; Estevez, C.; Orchard, M., “State-of-Charge Estimation to Improve Decision-making by MAC protocols used in WSNs,” Electronics Letters , vol. 55, Issue 3, pp. 161-163, 2019. DOI: https://doi.org/10.1049/el.2018.7666
Acuña, D.; Orchard, M.; Saona, R., “Conditional Predictive Bayesian Cramer-Rao Lower Bounds for Prognostic Algorithms Design,” Applied Soft Computing, vol. 72, pp. 647-665, 2018. DOI: https://doi.org/10.1016/j.asoc.2018.01.033
Espinoza, S.; Silva, J.; Mendez, R.; Lobos, R.; Orchard, M., “Optimality of the Maximum Likelihood estimator in Astrometry,” Astronomy and Astrophysics, vol. 616, A95, 2018. DOI: https://doi.org/10.1051/0004-6361/201732537
Pérez, A.; Quintero, V.; Jaramillo, F.; Rozas, H.; Jimenez, D.; Orchard, M.; Moreno, R., “Characterization of the Degradation Process of Lithium-ion Batteries when Discharged at Different Current Rates,” Proceedings of the iMechE, Part I: Journal of Systems and Control Engineering, vol. 232, Issue 8, pp. 1075-1089, 2018. DOI: https://doi.org/10.1177/0959651818774481
Pérez, A.; Benavides, M.; Rozas, H.; Seria, S.; Orchard, M., “Guidelines for the Characterization of the Internal Impedance of Lithium-Ion Batteries in PHM Algorithms,” International Journal of Prognostics and Health Management, vol. 9, Issue 1, pp. 1-10, 2018.
Jaramillo, F.; Orchard, M.; Muñoz, C.; Zamorano, M.; Antileo, C., “Advanced strategies to improve nitrification process in sequencing batch reactors – A review,” Journal of Environmental Management, vol.218, pp. 154-164, 2018. DOI: https://doi.org/10.1016/j.jenvman.2018.04.019
Tobar, F.; Castro, I.; Silva, F.; and Orchard, M., “Improving Battery Voltage Prediction in an Electric Bicycle Using Altitude Measurements and Kernel Adaptive Filters,” Pattern Recognition Letters, vol. 105, pp. 200-206, 2018. DOI: https://doi.org/10.1016/j.patrec.2017.09.009
Jaras, I.; Orchard, M., “Performance Assessment of Sequential Bayesian Processors based on Probably Approximately Correct Computation and Information Theory,” Electronics Letters, vol. 54, Nro. 6, pp. 357-359, 2018. DOI: https://doi.org/10.1049/el.2017.4159
Jaramillo, F.; Orchard, M.; Munoz, C.; Antileo, C.; Saez, D.; and Espinoza, P., “On-line estimation of the aerobic phase length for partial nitrification processes in SBR based on features extraction and SVM classification,” Chemical Engineering Journal, vol. 331, pp. 114-123, 2018. DOI: https://doi.org/10.1016/j.cej.2017.07.185
Mendez, R.; Clavería, R.; Orchard, M.; and Silva, J., “Orbits for eighteen visual binaries and two double-line spectroscopic binaries observed with HRCAM on the CTIO SOAR 4m telescope, using a new Bayesian orbit code based on Monte-Carlo Markov-Chain,” The Astronomical Journal, vol. 154, No. 5, 2017. DOI: https://doi.org/10.3847/1538-3881/aa8d6f
Barrios, P.; Adams, M.; Leung, K.; Inostroza; F.; Naqvi, G.; and Orchard, M., “Metrics for Evaluating Robotic Feature based Mapping Performance,” IEEE Transactions on Robotics, vol. 33, Issue 1, pp. 198-213, 2017. DOI: https://doi.org/10.1109/TRO.2016.2627027
Aguila-Camacho, N.; Duarte-Mermoud, M.; Le Roux, J.; and Orchard, M., “Control of a Grinding Mill Circuit using Simple Fractional Order Controllers,” Journal of Process Control, vol. 53, pp. 80-94, 2017. DOI: https://doi.org/10.1016/j.jprocont.2017.02.012
Torres, B.; Quintero, V.; Estevez, C.; Orchard, M.; Azurdia, C., “SoC Control for Improved Battery Life and Throughput Performance under VST-TDMA,” Electronics Letters, vol. 53, Issue 3, pp. 183-185, 2017. DOI: https://doi.org/10.1049/el.2016.3659
Acuña, D.; and Orchard, M., “Particle-Filtering-Based Failure Prognosis via Sigma-Points: Application to Lithium-Ion Battery State-of-Charge Monitoring,” Mechanical Systems and Signal Processing, vol. 85. pp. 827-848, 2017. DOI: https://doi.org/10.1016/j.ymssp.2016.08.029
Ley, C. and Orchard, M., “Chi-squared smoothed adaptive particle-filtering based prognosis,” Mechanical Systems and Signal Processing, vol. 82, pp. 148–165, 2017. DOI: https://doi.org/10.1016/j.ymssp.2016.05.015
Roje, T.; Marín, L.; Sáez, D.; Orchard, M.; and Jiménez-Estévez, G., “Consumption modeling based on Markov chains and Bayesian networks for a demand side management design of isolated microgrids,” International Journal of Energy Research, vol. 41, Issue 3, pp. 365-376, 2017. DOI: https://doi.org/10.1002/er.3607
Díaz, M.; Zagal, J.C.; Falcon, C.; Stepanova, M.; Valdivia, J.A.; Martínez-Ledesma, M.; Díaz, J.; Romanova, N.; Pacheco, E.; Milla, M.; Orchard, M.; Silva, J.; Mena, F.P.; and Jaramillo, F., “New opportunities offered by cubesats for space research in Latin America: the SUCHAI project case,” Advances in Space Research, vol. 58, Issue 10, pp. 2134–2147, 2016. DOI: https://doi.org/10.1016/j.asr.2016.06.012
Echeverría, A.; Silva, J.; Mendez, R.; and Orchard, M., “Analysis of the Bayesian Cramér-Rao lower bound in Astrometry: Studying the impact of prior information in the location of an object,” Astronomy and Astrophysics, vol. 594, A111, 2016. DOI: https://doi.org/10.1051/0004-6361/201628220
Perez, A.; Moreno, R.; Moreira, R.; Orchard, M.; and Strbac, G., “Effect of Battery Degradation on Multi-Service Portfolios of Energy Storage,” IEEE Transactions on Sustainable Energy, vol. 7, Issue 4, pp. 1718-1729, 2016. DOI: https://doi.org/10.1109/TSTE.2016.2589943
Mundnich, K. and Orchard, M., “Early online detection of high volatility clusters using Particle Filters,” Expert Systems with Applications, 54: 228–240, 2016. DOI: https://doi.org/10.1016/j.eswa.2016.01.052
Reyes-Marambio, J.; Moser, F.; Gana, F.; Severino, B.; Calderón-Muñoz, W.; Palma-Behnke, R.; Estevez, P.; Orchard, M.; Cortés, M., “A fractal time thermal model for predicting the surface temperature of air-cooled cylindrical Li-ion cells based on experimental measurements,” Journal of Power Sources, 161:349-363, 2016. DOI: https://doi.org/10.1016/j.jpowsour.2015.12.037
Burgos, C.; Orchard, M.; Kazerani, M.; Cárdenas, R.; and Sáez, D., “Particle-Filtering-Based Estimation of Maximum Available Power State in Lithium-Ion Batteries,” Applied Energy, 161:349-363, 2016. DOI: https://doi.org/10.1016/j.apenergy.2015.09.092
Lobos, R.; Silva, J.; Mendez, R.; and Orchard, M., “Performance analysis of the Least-Squares estimator in astrometry,” Publications of the Astronomical Society of the Pacific, 127: 1166-1182, Nov. 2015. DOI: https://doi.org/10.1086/683841
Pola, D.; Navarrete, H.; Orchard, M.; Rabié, R.; Cerda, M.; Olivares, B.; Silva, J.; Espinoza, P.; and Pérez, A., “Particle-filtering-based Discharge Time Prognosis for Lithium-Ion Batteries with a Statistical Characterization of Use Profiles,” IEEE Transactions on Reliability, vol. 64, Issue 2, pp. 710-720, June 2015. DOI: https://doi.org/10.1109/TR.2014.2385069
Orchard, M.; Lacalle, M.; Olivares, B.; Silva, J.; Palma, R.; Estévez, P.; Severino, B.; Calderon-Muñoz, W.; and Cortés M., “Information-Theoretic Measures and Sequential Monte Carlo Methods for Detection of Regeneration Phenomena in the Degradation of Lithium-Ion Battery Cells,” IEEE Transactions on Reliability, vol. 64, Issue 2, pp. 701-709, June 2015. DOI: https://doi.org/10.1109/TR.2015.2394356
Acuña, D.; Orchard, M.; Silva, F.; and Perez A., “Multiple-imputation-particle-filtering for Uncertainty Characterization in Battery State-of-Charge Estimation Problems with Missing Measurement Data: Performance Analysis and Impact on Prognostic Algorithms,” International Journal of Prognostics and Health Management, Vol. 6, (008), pp. 1-12, 2015.
Burgos, C.; Sáez, D.; Orchard, M.; and Cárdenas, R., “Fuzzy Modelling for the State-of-Charge Estimation of Lead-Acid Batteries,” Journal of Power Sources, vol. 274, pp. 355-366, Jan 2015. DOI: https://doi.org/10.1016/j.jpowsour.2014.10.036
Moya, J.; Ruiz-del-Solar, J.; Orchard, M.; Parra-Tsunekawa, I., “Fall Detection and Damage Reduction in Biped Humanoid Robots,” International Journal of Humanoid Robotics, vol. 12, Issue 1, Jan 2015. DOI: https://doi.org/10.1142/S0219843615500012
Severino, B.; Gana, F.; Palma-Behnke, R.; Estévez, P.; Calderón, W.; Orchard, M.; Cortés, M.; Reyes, J., “Multi-objective optimal design of lithium-ion battery packs based on evolutionary algorithms,” Journal of Power Sources, vol. 26, Issue 1, pp. 288-299, December 2014. DOI: https://doi.org/10.1016/j.jpowsour.2014.05.088
Zhang, B.; Orchard, M.; Saha, B.; Saxena, A.; Jin, Y.; and Vachtsevanos, G., “A Verification Framework with Application to a Propulsion System,” Expert Systems with Applications, vol. 41, Issue 13, pp. 5669-5679, October 2014. DOI: https://doi.org/10.1016/j.eswa.2014.03.017
Mundnich, K.; Orchard, M.; Silva, J.; Parada, P., “Volatility Estimation of Financial Returns using Risk-Sensitive Particle Filters,” Studies in Informatics and Control, Vol. 22, No. 3, pp. 297-306, September 2013. DOI: https://doi.org/10.24846/v22i3y201306
Orchard, M. and Hevia-Koch, P., “Risk Measures for Particle-filtering-based State-of-Charge Prognosis in Lithium-Ion Batteries,” IEEE Transactions on Industrial Electronics, vol. 60, No. 11, pp.5260-5269, November 2013. DOI: https://doi.org/10.1109/TIE.2012.2224079
Olivares, B.; Cerda, M.; Orchard, M.; and Silva, J., “Particle-filtering-based Prognosis Framework for Energy Storage Devices with a Statistical Characterization of State-of-Health Regeneration Phenomena,” IEEE Transactions on Instrumentation & Measurement, vol. 62, Issue 2, pp. 364‑376, February 2013. DOI: https://doi.org/10.1109/TIM.2012.2215142
Orchard, M.; Cerda, M.; Olivares, B.; and Silva, J., “Sequential Monte Carlo Methods for Discharge Time Prognosis in Lithium-Ion Batteries,” International Journal of Prognostics and Health Management, Vol. 3, Issue 2 (010), pp. 1-12, 2012.
Chen, C.; Brown, D.; Sconyers, C.; Zhang, B.; Vachtsevanos, G.; and Orchard, M., “An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems,” Expert Systems with Applications, vol. 39, Issue 10, pp. 9031‑9040, August 2012. DOI: https://doi.org/10.1016/j.eswa.2012.02.050
Chen, C.; Vachtsevanos, G.; Orchard, M., “Machine Remaining Useful Life Prediction: an Integrated Adaptive Neuro-Fuzzy and High-Order Particle Filtering Approach,” Mechanical Systems and Signal Processing, vol. 28, pp. 597-607, April 2012. DOI: https://doi.org/10.1016/j.ymssp.2011.10.009
Tobar, F. and Orchard, M., “Study of Financial Systems Volatility Using Suboptimal Estimation Algorithms,” Studies in Informatics and Control, vol. 21, Issue 1, pp. 59‑66, March 2012. DOI: https://doi.org/10.24846/v21i1y201207
Chen, C.; Vachtsevanos, G.; Orchard, M., “Machine Condition Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering,” IEEE Transactions on Industrial Electronics, vol. 58, no. 9, pp. 4353-4364, September 2011. DOI: https://doi.org/10.1109/TIE.2010.2098369
Mascaró, M.; Cabello, F.; Parra, S.I.; Vallejos, P.; Guerrero, P.; Ehrenfeld, A.; Acuña, A.; Ruiz del Solar, J.; and Orchard, M., “Instrumentación, Actuación e Implementación de Control Difuso de Bajo Nivel en Vehículo Terrestre Autónomo de Escala Real,” Anales del Instituto de Ingenieros de Chile, Vol. 123, no. 2, pp.53-61, Aug. 2011.
Zhang, B.; Sconyers, C.; Byington, C.; Patrick, R.; Orchard, M.; and Vachtsevanos, G., “A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection,” IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 2011-2018, May 2011. DOI: https://doi.org/10.1109/TIE.2010.2058072
Orchard, M.; Tang, L.; Saha, B.; Goebel, K.; and Vachtsevanos, G., “Risk-Sensitive Particle-Filtering-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices,” Studies in Informatics and Control, vol. 19, Issue 3, pp. 209-218, September 2010. DOI: https://doi.org/10.24846/v19i3y201001
Zhang, B.; Khawaja, T.; Patrick, R.; Vachtsevanos, G.; Orchard, M., and Saxena, A., “A Novel Blind Deconvolution De-Noising Scheme in Failure Prognosis,” Transactions of the Institute of Measurement and Control, vol. 32, Issue 1, pp. 3-30, February 2010. DOI: https://doi.org/10.1177/0142331209357844 (PDF)
Orchard, M.; Tobar, F.; and Vachtsevanos, G., “Outer Feedback Correction Loops in Particle Filtering-based Prognostic Algorithms: Statistical Performance Comparison,” Studies in Informatics and Control, vol. 18, Issue 4, pp. 295-304, December 2009. (PDF)
Orchard, M. and Vachtsevanos, G., “A Particle Filtering Approach for On-Line Fault Diagnosis and Failure Prognosis,” Transactions of the Institute of Measurement and Control, vol. 31, no. 3-4, pp. 221-246, June 2009. DOI: https://doi.org/10.1177/0142331208092026 (PDF)
Zhang, B.; Khawaja, T.; Patrick, R.; Vachtsevanos, G.; Orchard, M., and Saxena, A., “Application of Blind Deconvolution Denoising in Failure Prognosis,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 2, pp. 303-310, February 2009. DOI: https://doi.org/10.1109/TIM.2008.2005963 (PDF)
Orchard, M., and Vachtsevanos, G., “A Particle Filtering Approach for On-Line Failure Prognosis in a Planetary Carrier Plate,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 7, no. 4, pp. 221-227, 2007.
Gonzalez G.D.; Orchard, M.; Cerda J.L.; Casali A.; and Vallebuona, G., “Local models for soft-sensors in a rougher flotation bank,” Minerals Engineering, vol. 16, no.5, pp. 441-453, 2003. DOI: https://doi.org/10.1016/S0892-6875(03)00021-9
Dixon, J.; del Valle, Y.; Orchard, M.; Ortúzar, M.; Morán, L.; and Maffrand C., “A Full Compensating System for General Loads, Based on a Combination of Thyristor Binary Compensator, and a PWM-IGBT Active Power Filter,” IEEE Transactions on Industrial Electronics, vol. 50, no. 5, pp. 982-989, 2003. DOI: https://doi.org/10.1109/TIE.2003.817604