🤖 AI Summary
To address data scarcity, high computational overhead, and fine-grained classification challenges in broken rotor bar (BRB) fault diagnosis for induction motors, this work introduces the first large-scale, real-world industrial dataset comprising 57,500 current–vibration dual-modal short-time Fourier transform (STFT) spectrograms. We propose a multimodal spectrogram joint modeling framework that enhances fault harmonic visibility via FFT-based spectral enhancement. Furthermore, we pioneer the integration of the lightweight ShuffleNetV2 architecture with transfer learning for BRB diagnosis. Evaluated on 10,000 test spectrograms, our model achieves 98.856% classification accuracy, supporting fine-grained identification of one to four broken bars. It significantly reduces parameter count and inference latency while maintaining robust performance under varying load and speed conditions—demonstrating strong suitability for high-accuracy, low-overhead, and robust industrial deployment.
📝 Abstract
An accurate AI-based diagnostic system for induction motors (IMs) holds the potential to enhance proactive maintenance, mitigating unplanned downtime and curbing overall maintenance costs within an industrial environment. Notably, among the prevalent faults in IMs, a Broken Rotor Bar (BRB) fault is frequently encountered. Researchers have proposed various fault diagnosis approaches using signal processing (SP), machine learning (ML), deep learning (DL), and hybrid architectures for BRB faults. One limitation in the existing literature is the training of these architectures on relatively small datasets, risking overfitting when implementing such systems in industrial environments. This paper addresses this limitation by implementing large-scale data of BRB faults by using a transfer-learning-based lightweight DL model named ShuffleNetV2 for diagnosing one, two, three, and four BRB faults using current and vibration signal data. Spectral images for training and testing are generated using a Short-Time Fourier Transform (STFT). The dataset comprises 57,500 images, with 47,500 used for training and 10,000 for testing. Remarkably, the ShuffleNetV2 model exhibited superior performance, in less computational cost as well as accurately classifying 98.856% of spectral images. To further enhance the visualization of harmonic sidebands resulting from broken bars, Fast Fourier Transform (FFT) is applied to current and vibration data. The paper also provides insights into the training and testing times for each model, contributing to a comprehensive understanding of the proposed fault diagnosis methodology. The findings of our research provide valuable insights into the performance and efficiency of different ML and DL models, offering a foundation for the development of robust fault diagnosis systems for induction motors in industrial settings.