A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data

A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data

Anomaly detection (AD) tasks have been solved using machine learning algorithms in various domains and applications. The great majority of these algorithms use normal data to train a residual-based model, and assign anomaly scores to unseen samples based on their dissimilarity with the learned norma...

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Bibliographic Details
Main Authors: Markus Ulmer, Jannik Zgraggen, Lilach Goren Huber
Format: Article
Language:English
Published: The Prognostics and Health Management Society 2024-01-01
Series:International Journal of Prognostics and Health Management
Subjects:
fully unsupervised learning
deep learning
machine learning
anomaly detection
contaminated data
data refinement
time series
fault detection
turbofan engine
acoustic sensor data
Online Access:https://papers.phmsociety.org/index.php/ijphm/article/view/3589

Internet

https://papers.phmsociety.org/index.php/ijphm/article/view/3589

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