Fault Diagnosis of Inverter Current Sensor Using Artificial Neural Network Considering Out-of-distribution

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.