@inproceedings{1a66296a464d4b2fa8bf3e0c0675f02c,
title = "Fault Diagnosis of Inverter Current Sensor Using Artificial Neural Network Considering Out-of-distribution",
abstract = "This paper presents an Artificial Neural Network (ANN)-based approach for effectively diagnosing current sensor faults in inverters under various operating conditions. In this approach, additional techniques to make a robust fault classifier for untrained out-of-distribution (OOD) data are considered. The ANN is trained and evaluated through the Python TensorFlow library. The fault diagnosis accuracy is measured using the confusion matrix on the in-distribution dataset (current sensor faults data), and the degree of performance improvement on the OOD dataset is evaluated through the Area Under the Receiver Operating Characteristic (AUROC). The proposed method has been verified to have a classification accuracy of 98.94% on the in-distribution dataset and improve the AUROC from 0.9170 to 0.9866.",
keywords = "artificial neural network, fault detection, fault diagnosis, in-distribution data, out-of distribution data",
author = "Shim, {Jae Hoon} and Kahyun Lee and Ha, {Jung Ik}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; null ; Conference date: 24-05-2021 Through 27-05-2021",
year = "2021",
month = may,
day = "24",
doi = "10.1109/ECCE-Asia49820.2021.9479085",
language = "English",
series = "Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1520--1525",
booktitle = "Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021",
}