Work

Multi-Level Short Circuit Fault Detection in Induction Motors Using Deep CNN-LSTM Networks for Industry 4.0 Applications

Year: 2026

Type: article

Abstract: The reliability and efficiency of induction motors in Industry 4.0 environments critically depend on advanced fault detection systems capable of real-time monitoring and diagnosis. This paper presents a novel deep learning approach combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for automated detection and classification of inter-turn short-circuit faults in three-phase induction motors. Our methodology processes three-phase current signals through a sophisticated CNN-LSTM architecture that extracts both spatial and temporal fault patterns. The proposed system classifies seven distinct motor conditions: healthy operation, three levels of high-impedance faults (HI-1 to HI-3), and three levels of low-impedance faults (LI-1 to LI-3). Experimental validation demonstrates exceptional performance, with the CNN-LSTM model achieving 97.2% accuracy, significantly outperforming traditional machine learning approaches, including SVM (66.3%), Random Forest (67.4%), and KNN (78.1%). The system provides real-time fault classification with inference times under 3 ms, making it suitable for continuous monitoring in smart manufacturing environments.

Source: Eng—Advances in Engineering

DOI: 10.3390/eng7020094

Authors: Jalila Kaouthar Kammoun, Hanen Lajnef, Mourad Fakhfakh

Institutions: University of Sfax, National School of Electronics and Telecommunications of Sfax, Tunisia, University of Carthage

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,448