Multi-task Multi-modal Models for Collective Anomaly Detection

2017 ◽  
Author(s):  
Tsuyoshi Ide ◽  
Dzung T. Phan ◽  
Jayant Kalagnanam
Keyword(s):  
Anomaly Detection ◽  
Collective Anomaly

EagleMine: Vision-guided Micro-clusters recognition and collective anomaly detection

2021 ◽  
Vol 115 ◽  
pp. 236-250
Author(s):  
Wenjie Feng ◽  
Shenghua Liu ◽  
Christos Faloutsos ◽  
Bryan Hooi ◽  
Huawei Shen ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Collective Anomaly

scholarly journals A Novel Vehicle Gearbox Fault Diagnosis Approach Based on Collective Anomaly Detection

2021 ◽  
Vol 252 ◽  
pp. 01052
Author(s):  
Zhongfeng Hu ◽  
Xiaodi Huang
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
Data Distribution ◽  
Likelihood Estimate ◽  
Iteration Algorithm ◽  
Multivariate Gaussian Distribution ◽  
Test Dataset ◽  
Sample Data ◽  
Using Data ◽  
Collective Anomaly

Targeting the problem of gearbox fault diagnosis, we proposed a novel semi-supervised approach based on collective anomaly detection. Based on the limited sample data, the principle of the approach is to detect whether a test dataset contains abnormal patterns by using data distribution as the metric. The sequence obeying unexpected distribution will be identified as collective anomaly, which may be generated by fault patterns. The approach consists of three steps. First, the mixture of multivariate Gaussian distribution is used to fit the structure of sample dataset and test dataset. Then, based on maximum likelihood estimate algorithm, we hope to search the optimal parameters which can fit the data distribution with the highest degree. Finally, the fixed point iteration algorithm is used to solve likelihood estimate functions. Experimental results demonstrate that the proposed approach can be used to find fault patterns of gearbox without the prior knowledge of their generated mechanisms.

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