Spatial Activation Suppression for Unsupervised Anomaly Detectors in Freight Train Fault Detection
Author:
Affiliation:
1. State Key Laboratory of Advanced Rail Autonomous Operation and the School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, Beijing, China
2. College of Engineering, University of Wisconsin-Madison, Madison, WI, USA
Funder
National Natural Science Foundation of China
State Key Laboratory of Advanced Rail Autonomous Operation
Technology Development Program of China Energy Investment Corporation
Xiong’an Science and Technology Innovation Special Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10367905/10364965.pdf?arnumber=10364965
Reference51 articles.
1. Missing Small Fastener Detection Using Deep Learning
2. Real-Time Vision-Based System of Fault Detection for Freight Trains
3. Unsupervised Machine Anomaly Detection Using Autoencoder and Temporal Convolutional Network
4. Unsupervised Anomaly Segmentation Via Multilevel Image Reconstruction and Adaptive Attention-Level Transition
5. Unsupervised Deep Multitask Anomaly Detection With Robust Alarm Strategy for Online Evaluation of Bearing Early Fault Occurrence
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