M
Michael Pecht
Researcher at University of Maryland, College Park
Publications - 1194
Citations - 38587
Michael Pecht is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Prognostics & Reliability (statistics). The author has an hindex of 78, co-authored 1131 publications receiving 29099 citations. Previous affiliations of Michael Pecht include City University of Hong Kong & American Society of Mechanical Engineers.
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Prognostics and health management of electronics
Nikhil M. Vichare,Michael Pecht +1 more
TL;DR: The state-of-the-art in the area of electronics prognostics and health management can be found in this article, where four current approaches include built-in-test (BIT), use of fuses and canary devices, monitoring and reasoning of failure precursors, and modeling accumulated damage based on measured life-cycle loads.
Book
Prognostics and Health Management of Electronics
TL;DR: In this paper, a physics of failure (PoF) based approach is proposed for the prediction of the future state of reliability of a system under its actual application conditions, which integrates sensor data with models that enable in situ assessment of the deviation or degradation of a product from an expected normal operating condition.
Journal ArticleDOI
Light emitting diodes reliability review
Moon-Hwan Chang,Diganta Das,Prabhakar V. Varde,Prabhakar V. Varde,Michael Pecht,Michael Pecht +5 more
TL;DR: This paper provides the groundwork for an understanding of the reliability issues of LEDs across the supply chain and identifies the relationships between failure causes and their associated mechanisms, issues in thermal standardization, and critical areas of investigation and development in LED technology and reliability.
Journal ArticleDOI
Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
TL;DR: The developed method is able to predict the battery's RUL independent of offline training data, and when some offline data is available, the RUL can be predicted earlier than in the traditional methods.
Journal ArticleDOI
Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method
TL;DR: In this article, a new method for state of health (SOH) and remaining useful life (RUL) estimations for lithium-ion batteries using Dempster-Shafer theory (DST) and the Bayesian Monte Carlo (BMC) method is proposed.