26 citations · 44 across the 8 of their papers we have counts for
9 papers
Integrating Prognostics, Maintenance, and Tail Assignment under Remaining Useful Life Uncertainty: A Stochastic Optimisation Approach for Airline Reliability
Benno Käslin, Marta Ribeiro, Dimitrios Zarouchas +1
Ensuring reliability, safety, and economic efficiency in airline operations requires maintenance and fleet scheduling strategies that explicitly account for uncertainty in Remainin…
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…
A Benchmark on Uncertainty Quantification for Deep Learning Prognostics
Luis Basora, Arthur Viens, Manuel Arias Chao +1
Reliable uncertainty quantification on RUL prediction is crucial for informative decision-making in predictive maintenance. In this context, we assess some of the latest developmen…
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…