D2: Anomaly Detection and Diagnosis in Networked Embedded Systems by Program Profiling and Symptom Mining

2013 ◽  
Author(s):  
Wei Dong ◽  
Chun Chen ◽  
Jiajun Bu ◽  
Xue Liu ◽  
Yunhao Liu
Keyword(s):  
Embedded Systems ◽  
Anomaly Detection ◽  
Networked Embedded Systems ◽  
Program Profiling ◽  
Detection And Diagnosis

scholarly journals Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110131
Author(s):  
Junfeng Wu ◽  
Li Yao ◽  
Bin Liu ◽  
Zheyuan Ding ◽  
Lei Zhang
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Unified Framework ◽  
Diagnosis System ◽  
Learning Framework ◽  
Task Learning ◽  
Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

Modeling and Design of Fault-Tolerant and Self-Adaptive Reconfigurable Networked Embedded Systems

2006 ◽  
Vol 2006 ◽  
pp. 1-15 ◽  
Author(s):  
Thilo Streichert ◽  
Dirk Koch ◽  
Christian Haubelt ◽  
Jürgen Teich
Keyword(s):  
Embedded Systems ◽  
Fault Tolerant ◽  
Networked Embedded Systems ◽  
Self Adaptive
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