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  2022 (1)
A Multioutput Convolved Gaussian Process for Capacity Forecasting of Li-Ion Battery Cells. Chehade, A., A.; and Hussein, A., A. IEEE Transactions on Power Electronics, 37(1): 896-909. 1 2022.
A Multioutput Convolved Gaussian Process for Capacity Forecasting of Li-Ion Battery Cells [link]Website   link   bibtex  
  2021 (6)
A dual-LSTM framework combining change point detection and remaining useful life prediction. Shi, Z.; and Chehade, A. Reliability Engineering & System Safety, 205: 107257. 1 2021.
A dual-LSTM framework combining change point detection and remaining useful life prediction [link]Website   link   bibtex  
Dynamic Adherent Raindrop Simulator for Automotive Vision Systems. Hamzeh, Y.; El-Shair, Z., A.; Chehade, A.; and Rawashdeh, S., A. IEEE Access, 9: 114808-114820. 2021.
Dynamic Adherent Raindrop Simulator for Automotive Vision Systems [link]Website   link   bibtex  
RMOPP: Robust Multi-Objective Post-Processing for Effective Object Detection. Savargaonkar, M.; Chehade, A.; and Rawashdeh, S. . 2 2021.
RMOPP: Robust Multi-Objective Post-Processing for Effective Object Detection [link]Website   link   bibtex   abstract  
Sparse Autoencoded Long Short-Term Memory Network for State-of-Charge Estimations. Savargaonkar, M.; Oyewole, I.; and Chehade, A. In 2021 IEEE Transportation Electrification Conference & Expo (ITEC), pages 474-478, 6 2021. IEEE
Sparse Autoencoded Long Short-Term Memory Network for State-of-Charge Estimations [link]Website   link   bibtex  
A Hybrid Long Short-Term Memory Network for State-of-Charge Estimation of Li-ion Batteries. Oyewole, I.; Savargaonkar, M.; Chehade, A.; and Kim, Y. In 2021 IEEE Transportation Electrification Conference & Expo (ITEC), pages 469-473, 6 2021. IEEE
A Hybrid Long Short-Term Memory Network for State-of-Charge Estimation of Li-ion Batteries [link]Website   link   bibtex  
Conditional Gaussian Mixture Model for Warranty Claims Forecasting. Chehade, A.; Savargaonkar, M.; and Krivtsov, V. Reliability Engineering & System Safety,108180. 11 2021.
Conditional Gaussian Mixture Model for Warranty Claims Forecasting [link]Website   link   bibtex  
  2020 (8)
Data-driven Adaptive Thresholding Model for Real-time Valve Delay Estimation in Digital Pump/Motors. Chehade, A.; Breidi, F.; Pate, K., S.; and Lumkes, J. International Journal of Fluid Power, 20(3): 271–294. 3 2020.
Data-driven Adaptive Thresholding Model for Real-time Valve Delay Estimation in Digital Pump/Motors [link]Website   link   bibtex   abstract  
BLNN: An R package for training neural networks using Bayesian inference. Sharaf, T.; Williams, T.; Chehade, A.; and Pokhrel, K. SoftwareX, 11: 100432. 1 2020.
BLNN: An R package for training neural networks using Bayesian inference [link]Website   link   bibtex  
Accelerating the Discovery of New DP Steel Using Machine Learning-Based Multiscale Materials Simulations. Chehade, A., A.; Belgasam, T., M.; Ayoub, G.; and Zbib, H., M. Metallurgical and Materials Transactions A, 51(6): 3268-3279. 6 2020.
Accelerating the Discovery of New DP Steel Using Machine Learning-Based Multiscale Materials Simulations [link]Website   link   bibtex  
Robust Artificial Neural Network-Based Models for Accurate Surface Temperature Estimation of Batteries. Hussein, A., A.; and Chehade, A., A. IEEE Transactions on Industry Applications, 56(5): 5269-5278. 9 2020.
Robust Artificial Neural Network-Based Models for Accurate Surface Temperature Estimation of Batteries [link]Website   link   bibtex  
Power–law nonhomogeneous Poisson process with a mixture of latent common shape parameters. Chehade, A.; Shi, Z.; and Krivtsov, V. Reliability Engineering & System Safety, 203: 107097. 11 2020.
Power–law nonhomogeneous Poisson process with a mixture of latent common shape parameters [link]Website   link   bibtex  
A Cycle-based Recurrent Neural Network for State-of-Charge Estimation of Li-ion Battery Cells. Savargaonkar, M.; Chehade, A.; Shi, Z.; and Hussein, A., A. In 2020 IEEE Transportation Electrification Conference & Expo (ITEC), pages 584-587, 6 2020. IEEE
A Cycle-based Recurrent Neural Network for State-of-Charge Estimation of Li-ion Battery Cells [link]Website   link   bibtex  
An Adaptive Deep Neural Network with Transfer Learning for State-of-Charge Estimations of Battery Cells. Savargaonkar, M.; and Chehade, A. In 2020 IEEE Transportation Electrification Conference & Expo (ITEC), pages 598-602, 6 2020. IEEE
An Adaptive Deep Neural Network with Transfer Learning for State-of-Charge Estimations of Battery Cells [link]Website   link   bibtex  
A Long Short-Term Memory Network for Online State-of-Charge Estimation of Li-ion Battery Cells. Shi, Z.; Savargaonkar, M.; Chehade, A., A.; and Hussein, A., A. In 2020 IEEE Transportation Electrification Conference & Expo (ITEC), pages 594-597, 6 2020. IEEE
A Long Short-Term Memory Network for Online State-of-Charge Estimation of Li-ion Battery Cells [link]Website   link   bibtex  
  2019 (8)
Structural Degradation Modeling Framework for Sparse Data Sets With an Application on Alzheimer’s Disease. Chehade, A.; and Liu, K. IEEE Transactions on Automation Science and Engineering, 16(1): 192-205. 1 2019.
Structural Degradation Modeling Framework for Sparse Data Sets With an Application on Alzheimer’s Disease [link]Website   link   bibtex  
Sensor Fusion via Statistical Hypothesis Testing for Prognosis and Degradation Analysis. Chehade, A.; and Shi, Z. IEEE Transactions on Automation Science and Engineering, 16(4): 1774-1787. 10 2019.
Sensor Fusion via Statistical Hypothesis Testing for Prognosis and Degradation Analysis [link]Website   link   bibtex  
Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach. Chehade, A., A.; and Hussein, A., A. . 7 2019.
Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach [link]Website   link   bibtex   abstract  
A Multi-Output Convolved Gaussian Process Model for Capacity Estimation of Electric Vehicle Li-ion Battery Cells. Chehade, A., A.; and Hussein, A., A. In 2019 IEEE Transportation Electrification Conference and Expo (ITEC), pages 1-4, 6 2019. IEEE
A Multi-Output Convolved Gaussian Process Model for Capacity Estimation of Electric Vehicle Li-ion Battery Cells [link]Website   link   bibtex  
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion. Chehade, A.; and Shi, Z. . 10 2019.
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion [pdf]Paper   The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion [link]Website   link   bibtex   abstract  
Monitoring Digital Technologies in Hydraulic Systems Using CUSUM Control Charts. Breidi, F.; Chehade, A.; and Lumkes, J. In ASME/BATH 2019 Symposium on Fluid Power and Motion Control, 10 2019. American Society of Mechanical Engineers
Monitoring Digital Technologies in Hydraulic Systems Using CUSUM Control Charts [link]Website   link   bibtex  
The sparse reverse of principal component analysis for fast low-rank matrix completion. Chehade, A.; and Shi, Z. 2019.
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Latent function decomposition for forecasting li-ion battery cells capacity: A multi-output convolved gaussian process approach. Chehade, A.; and Hussein, A. 2019.
link   bibtex   abstract  
  2018 (2)
A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes. Chehade, A.; Song, C.; Liu, K.; Saxena, A.; and Zhang, X. Journal of Quality Technology, 50(2): 150-165. 4 2018.
A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes [link]Website   link   bibtex  
Design of a Transparent Hydraulic/Pneumatic Excavator Arm for Teaching and Outreach Activities. Pate, K.; Marx, J.; Chehade, A.; and Breidi, F. In 2018 ASEE Annual Conference & Exposition, 6 2018.
Design of a Transparent Hydraulic/Pneumatic Excavator Arm for Teaching and Outreach Activities [link]Website   link   bibtex  
  2017 (3)
Optimize the Signal Quality of the Composite Health Index via Data Fusion for Degradation Modeling and Prognostic Analysis. Liu, K.; Chehade, A.; and Song, C. IEEE Transactions on Automation Science and Engineering, 14(3): 1504-1514. 7 2017.
Optimize the Signal Quality of the Composite Health Index via Data Fusion for Degradation Modeling and Prognostic Analysis [link]Website   link   bibtex  
Sensory-Based Failure Threshold Estimation for Remaining Useful Life Prediction. Chehade, A.; Bonk, S.; and Liu, K. IEEE Transactions on Reliability, 66(3): 939-949. 9 2017.
Sensory-Based Failure Threshold Estimation for Remaining Useful Life Prediction [link]Website   link   bibtex  
Data-driven Approaches for Condition Monitoring and Predictive Analytics. Chehade, A. Ph.D. Thesis, 2017.
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  2014 (2)
Optimal dynamic behavior of adaptive WIP regulation with multiple modes of capacity adjustment. Chehade, A.; and Duffie, N. In Procedia CIRP, volume 19, pages 168-173, 2014. Elsevier
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Control theoretical modeling of transient behavior of production planning and control: A review. Duffie, N.; Chehade, A.; and Athavale, A. In Procedia CIRP, volume 17, pages 20-25, 2014. Elsevier
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  2012 (1)
Dynamics of autonomously acting products and work systems in production and assembly. Jeken, O.; Duffie, N.; Windt, K.; Blunck, H.; Chehade, A.; and Rekersbrink, H. CIRP Journal of Manufacturing Science and Technology, 5(4): 267-275. 2012.
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