Olga Fink

Fleet PHM for Critical Systems

2018 · PHM Society European Conference

Data-driven approaches are highly relying on the representativeness of the dataset used for training the algorithms. For Prognostics and Health Management (PHM) applications, a lack of representativeness will result in detecting new operating conditions (that …

Interpretable Prognostics with Concept Bottleneck Models

2024 · arXiv (Cornell University)

Deep learning approaches have recently been extensively explored for the prognostics of industrial assets. However, they still suffer from a lack of interpretability, which hinders their adoption in safety-critical applications. To improve their trustworthiness, explainable …

NNG-Mix: Improving Semi-Supervised Anomaly Detection With Pseudo-Anomaly Generation

2024 · IEEE Transactions on Neural Networks and Learning Systems

Anomaly detection (AD) is essential in identifying rare and often critical events in complex systems, finding applications in fields such as network intrusion detection, financial fraud detection, and fault detection in infrastructure and industrial systems. …