A generalizable anomaly detection framework with dynamic concept drift suppression for non-stationary time series

This publication has 23 references indexed in Scilit:

• Release Power of Mechanism and Data Fusion: A Hierarchical Strategy for Enhanced MIQ-Related Modeling and Fault Detection in BFIP
  IEEE/CAA Journal of Automatica Sinica, 2025
• Security and privacy of industrial big data: Motivation, opportunities, and challenges
  Journal of Network and Computer Applications, 2025
• Deep Koopman Predictors for Anomaly Detection of Complex IoT Systems With Time Series Data
  IEEE Internet of Things Journal, 2024
• TMANomaly: Time-Series Mutual Adversarial Networks for Industrial Anomaly Detection
  IEEE Transactions on Industrial Informatics, 2023
• Concept Drift Detection in Data Stream Mining : A literature review
  Journal of King Saud University Computer and Information Sciences, 2022
• Incremental Learning and Conditional Drift Adaptation for Nonstationary Industrial Process Fault Diagnosis
  IEEE Transactions on Industrial Informatics, 2022
• Integrated Generative Model for Industrial Anomaly Detection via Bidirectional LSTM and Attention Mechanism
  IEEE Transactions on Industrial Informatics, 2021
• A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder
  IEEE Robotics and Automation Letters, 2018
• Recognizing input space and target concept drifts in data streams with scarcely labeled and unlabelled instances
  Information Sciences, 2016
• Support Vector Data Description
  Machine Learning, 2004