Document Type

Article

Publication Date

2026

DOI

10.3390/machines14060693

Publication Title

Machines

Volume

14

Issue

6

Pages

693

Abstract

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on operating conditions, enabling the model to generalize across different load levels and motor power ratings without retraining. A lightweight BiLSTM architecture is employed, reducing computational complexity while maintaining high diagnostic performance. The proposed method is validated under various operating conditions, including different speeds, load variations, motor power ratings, and noisy conditions. The results demonstrate an overall classification accuracy of 99.65%, with reliable fault detection achieved within less than half of a fundamental cycle. The proposed approach provides an efficient, robust, and scalable solution for inverter fault detection and diagnosis, offering strong potential for practical deployment in modern motor drive systems.

Rights

© 2026 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author."

Original Publication Citation

Zamani Khaneghah, M., Alzayed, M., & Chaoui, H. (2026). Generalized inverter fault detection using normalized current features and a lightweight BiLSTM network. Machines, 14(6), Article 693. https://doi.org/10.3390/machines14060693

ORCID

0000-0001-8728-3653 (Chaoui)

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