12 citations · 16 across the 4 of their papers we have counts for
7 papers
Health Index Estimation Through Integration of General Knowledge with Unsupervised Learning
Kristupas Bajarunas, Marcia L. Baptista, Kai Goebel +1
Accurately estimating a Health Index (HI) from condition monitoring data (CM) is essential for reliable and interpretable prognostics and health management (PHM) in complex systems…
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…