Pecht, Michael

Deep Residual Shrinkage Networks for Fault Diagnosis

Zhao, M., Zhong, S., Fu, X., Tang, B., & Pecht, M. (2020) · IEEE Transactions on Industrial Informatics

Machine Fault Diagnosis TechniquesFault Detection and Control Systems

Cited by 1,447

Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 1,326

Battery Lifetime Prognostics

Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 1,320

Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method

He, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources

Battery Modeling and ManagementReliability and Maintenance Optimization

Cited by 1,105

State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures

Xing, Y., He, W., Pecht, M., & Tsui, K. L. (2014) · Applied Energy

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 977

Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries

Zheng, F., Xing, Y., Jiang, J., Sun, B., Kim, J., & Pecht, M. (2016) · Applied Energy

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 574

Battery management systems in electric and hybrid vehicles

Xing, Y., Ma, E. W. M., Tsui, K. L., & Pecht, M. (2011) · Energies

Battery Modeling and ManagementElectric and Hybrid Vehicle Technologies

Cited by 540

State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation

He, W., Williard, N., Chen, C., & Pecht, M. (2014) · International Journal of Electrical Power & Energy Systems

Battery Modeling and ManagementElectric and Hybrid Vehicle Technologies

Cited by 539

Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression

Liu, D., Pang, J., Zhou, J., Peng, Y., & Pecht, M. (2013) · Microelectronics Reliability

Battery Modeling and ManagementReliability and Maintenance Optimization

Cited by 443

A failure modes, mechanisms, and effects analysis (FMMEA) of lithium-ion batteries

Hendricks, C., Williard, N., Mathew, S., & Pecht, M. (2015) · Journal of Power Sources

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 381

Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes

Zhao, M., Kang, M., Tang, B., & Pecht, M. (2018) · IEEE Transactions on Industrial Electronics

Machine Fault Diagnosis TechniquesGear and Bearing Dynamics Analysis

Cited by 380

Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model

Wang, D., Miao, Q., & Pecht, M. (2013) · Journal of Power Sources

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 351

A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries

Xiong, R., Zhang, Y., He, H., Zhou, X., & Pecht, M. G. (2018) · IEEE Transactions on Industrial Electronics

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 285

Electronic part life cycle concepts and obsolescence forecasting

Pecht, M. G., Sandborn, P. A., & Solomon, R. (2000) · IEEE Transactions on Components and Packaging Technologies

Transportation Systems and InfrastructureInnovation Diffusion and Forecasting

Cited by 194

Sensor Systems for Prognostics and Health Management

Cheng, S., Azarian, M. H., & Pecht, M. G. (2010) · Sensors

Reliability and Maintenance OptimizationMachine Fault Diagnosis Techniques

Cited by 188

Multiple wavelet coefficients fusion in deep residual networks for fault diagnosis

Zhao, M., Kang, M., Tang, B., & Pecht, M. (2019) · IEEE Transactions on Industrial Electronics

Machine Fault Diagnosis TechniquesFault Detection and Control Systems

Cited by 174

Physics-of-Failure: An Approach to Reliable Product Development

Pecht, M., & Dasgupta, A. (1995) · Journal of the IEST

Reliability and Maintenance OptimizationIntegrated Circuits and Semiconductor Failure Analysis

Cited by 140

State of charge estimation for a parallel battery pack jointly by fuzzy-PI model regulator and adaptive unscented Kalman filter

Peng, S., Miao, Y., Xiong, R., Bai, J., Cheng, M., & Pecht, M. (2024) · Applied Energy

Battery Modeling and ManagementElectric and Hybrid Vehicle Technologies

Cited by 109

Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries

Li, J., Wang, D., Deng, L., Cui, Z., Lyu, C., Wang, L., & Pecht, M. (2020) · Journal of Energy Storage

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 99

Data driven analysis of lithium-ion battery internal resistance towards reliable state of health prediction

Hoque, M. A., Nurmi, P., Kumar, A., Varjonen, S., Song, J., Pecht, M. G., & Tarkoma, S. (2021) · Journal of Power Sources

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 85

Machine learning techniques suitability to estimate the retained capacity in lithium-ion batteries from partial charge/discharge curves

Beltran, H., Sansano, E., & Pecht, M. (2023) · Journal of Energy Storage

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 29

Life Extension of Electronic Products: A Case Study of Smartphones

Hazelwood, D. A., & Pecht, M. G. (2021) · IEEE Access

Recycling and Waste Management TechniquesSustainable Supply Chain Management

Cited by 28

State of health prediction based on multi-kernel relevance vector machine and whale optimization algorithm for lithium-ion battery

Wang, S., Zhang, X., Chen, W., Han, W., Zhou, S., & Pecht, M. (2026) · Transactions of the Institute of Measurement and Control

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 18

Advances in Sequential Monte Carlo methods for joint state and parameter estimation applied to prognostics

Sun, J., Zuo, H., & Pecht, M. G. (2011) · 2011 Prognostics and System Health Managment Confernece

Reliability and Maintenance OptimizationMarkov Chains and Monte Carlo Methods

Cited by 7