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Found 5 papers, 1 papers with code
Stability-Informed Initialization of Neural Ordinary Differential Equations
This paper addresses the training of Neural Ordinary Differential Equations (neural ODEs), and in particular explores the interplay between numerical integration techniques, stability regions, step size, and initialization techniques.
Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems
Data-driven modeling and machine learning are widely used to model the behavior of dynamic systems.
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data
The use of general-purpose multi-class classification methods for fault diagnosis is complicated by imbalanced training data and unknown fault classes.
Residual Generation Using Physically-Based Grey-Box Recurrent Neural Networks For Engine Fault Diagnosis
Residual generation using grey-box recurrent neural networks can be used for anomaly classification where physical insights about the monitored system are incorporated into the design of the machine learning algorithm.
Isolation and Localization of Unknown Faults Using Neural Network-Based Residuals
In model-based diagnosis, physical-based models are used to create residuals that isolate faults by mapping model equations to faulty system components.
