Adam Thelen

TL;DR: In this article, a physics-informed deep learning approach was proposed for bearing condition monitoring and fault detection, which consists of a simple threshold model and a deep convolutional neural network (CNN) model.

TL;DR: In this paper , the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins are examined, and a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.

TL;DR: In this article , the authors proposed and extensively test two light-weight physics-informed machine learning methods for online estimating the capacity of a battery cell and diagnosing its primary degradation modes using only limited early-life experimental degradation data.

TL;DR: In this paper , the authors examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins and present a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantization, optimization methods, open-source datasets and tools, major findings, challenges, and future directions.

TL;DR: In this paper , the authors proposed a model reference adaptive system (MRAS) for high-rate structural health monitoring (HRSHM) to empower sub-millisecond decision systems, which is a complex task because of large uncertainties in the external loads, high levels of nonstationarities and heavy disturbances.