133 citations · 200 across the 10 of their papers we have counts for
10 papers
Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers
Chi-Ching Hsu, Gaëtan Frusque, Florent Forest +3
Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds.…
Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation
Keivan Faghih Niresi, Lucas Kuhn, Gaëtan Frusque +1
Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby fa…
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion
Gaëtan Frusque, Ismail Nejjar, Majid Nabavi +1
The Health Index (HI) is crucial for evaluating system health and is important for tasks like anomaly detection and Remaining Useful Life (RUL) prediction of safety-critical system…
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
Hao Dong, Gaëtan Frusque, Yue Zhao +2
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…
Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios
Baorui Dai, Gaëtan Frusque, Tianfu Li +2
The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operationa…
Robust Time Series Denoising with Learnable Wavelet Packet Transform
Gaetan Frusque, Olga Fink
Signal denoising is a key preprocessing step for many applications, as the performance of a learning task is closely related to the quality of the input data. In this paper, we app…
Acceleration-guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring
Baorui Dai, Gaëtan Frusque, Qi Li +1
Acoustic monitoring has recently shown great potential in the diagnosis of infrastructure condition. However, due to the severe noise interference in acoustic signals, meaningful f…
Learnable Wavelet Packet Transform for Data-Adapted Spectrograms
Gaetan Frusque, Olga Fink
Capturing high-frequency data concerning the condition of complex systems, e.g. by acoustic monitoring, has become increasingly prevalent. Such high-frequency signals typically con…
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection
Gaetan Frusque, Gabriel Michau, Olga Fink
Acoustic monitoring for machine fault detection is a recent and expanding research path that has already provided promising results for industries. However, it is impossible to col…
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series
Gabriel Michau, Gaetan Frusque, Olga Fink
High-Frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep learning tools are designed for inputs of fix…