Open AccessMILAD: Robust Anomaly Detection for Electric Vehicles with Label Noise
Author(s) -
Yu Ye,
Bailin Feng,
Wujun Tao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2132/1/012047
Subject(s) - anomaly detection , noise (video) , anomaly (physics) , computer science , process (computing) , hazard , artificial intelligence , image (mathematics) , physics , chemistry , organic chemistry , condensed matter physics , operating system
One of the bottlenecks restricting the development of electric vehicle industry is the safety problem. Although numerous of anomaly detection algorithms for electric vehicles have been proposed, most of them may perform poorly due to the complexity and unpredictability of real scenes. We consider that there may be a certain degree of potential safety hazard in the battery system of electric vehicles before, during and after the process of faults in the real scenes, that is, label noise. In order to solve this problem, we propose a Multi-Instance Learning based Anomaly Detection (MILAD) framework, to perform anomaly detection for electric vehicles with label noise problem. Extensive cross validation experiments fully verify that the framework can effectively detect the existence of abnormal conditions in the presence of label noise in multivariate time series data.