26 citations · 57 across the 8 of their papers we have counts for
9 papers
Prescribing Optimal Health-Aware Operation for Urban Air Mobility with Deep Reinforcement Learning
Mina Montazeri, Chetan Kulkarni, Olga Fink
Urban Air Mobility (UAM) aims to expand existing transportation networks in metropolitan areas by offering short flights either to transport passengers or cargo. Electric vertical…
Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction
Luca Biggio, Tommaso Bendinelli, Chetan Kulkarni +1
Electrochemical batteries are ubiquitous devices in our society. When they are employed in mission-critical applications, the ability to precisely predict the end of discharge unde…
A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning
Yuan Tian, Minghao Han, Chetan Kulkarni +1
Prescribing optimal operation based on the condition of the system and, thereby, potentially prolonging the remaining useful lifetime has a large potential for actively managing th…
Uncertainty-aware Remaining Useful Life predictor
Luca Biggio, Alexander Wieland, Manuel Arias Chao +2
Remaining Useful Life (RUL) estimation is the problem of inferring how long a certain industrial asset can be expected to operate within its defined specifications. Deploying succe…
Battery Model Calibration with Deep Reinforcement Learning
Ajaykumar Unagar, Yuan Tian, Manuel Arias-Chao +1
Lithium-Ion (Li-I) batteries have recently become pervasive and are used in many physical assets. To enable a good prediction of the end of discharge of batteries, detailed electro…
Real-Time Model Calibration with Deep Reinforcement Learning
Yuan Tian, Manuel Arias Chao, Chetan Kulkarni +2
The dynamic, real-time, and accurate inference of model parameters from empirical data is of great importance in many scientific and engineering disciplines that use computational…
Fusing Physics-based and Deep Learning Models for Prognostics
Manuel Arias Chao, Chetan Kulkarni, Kai Goebel +1
Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world…
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders
Manuel Arias Chao, Bryan T. Adey, Olga Fink
Data-driven fault diagnostics of safety-critical systems often faces the challenge of a complete lack of labeled data associated with faulty system conditions (i.e., fault types) a…
Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models
Manuel Arias Chao, Chetan Kulkarni, Kai Goebel +1
With the increased availability of condition monitoring data and the increased complexity of explicit system physics-based models, the application of data-driven approaches for fau…