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Low-Frequency Adaptation-Deep Neural Network-Based Domain Adaptation Approach for Shaft Imbalance Fault Diagnosis

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Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Purpose

This paper proposes a Low-Frequency Adaptation-Deep Neural Network (LFA-DNN) architecture for the classification of shaft imbalance in rotatory machinery. Additionally, a Low-Frequency Adaptation-Convolution Autoencoder (LFA-CA) is proposed to address the issue of industrial noise in the signals. The paper also proposes a deep neural network-based domain adaptation approach to tackle the problem of domain shift in the feature spaces of different datasets due to fluctuating operating conditions.

Methods

The LFA-DNN architecture is designed to extract low-frequency features from vibrational data and diagnose the presence of imbalance. The LFA-CA is specifically designed to extract signal features and reconstruct them. Domain adaptation is proposed to adapt the feature spaces of classes of one dataset to another dataset with different operating conditions. The proposed methodology is evaluated on a dataset collected experimentally with varying amounts of mass unbalance.

Results

The proposed LFA-DNN and LFA-CA architectures are effective in classifying shaft imbalance faults and denoising the signals, respectively. The proposed domain adaptation approach successfully reduces the Maximum Mean Discrepancy (MMD) between feature space distributions, thereby improving the accuracy of the model in real-world industrial settings.

Conclusions

The proposed methodology is effective in detecting and diagnosing shaft imbalance faults in industrial rotating machinery, despite the challenges of fluctuating operating conditions and industrial noise in the signals. The domain adaptation approach is effective in reducing the domain shift problem and improving the accuracy of the model. This paper provides a comprehensive approach to tackle the problem of shaft imbalance faults, which can enhance the overall product quality and the safety of workers in industrial settings.

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  1. Thapar Institute of Engineering and Technology, Patiala, India

    Jatin Kumar Arora, Sudhar Rajagopalan, Jaskaran Singh & Ashish Purohit

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Arora, J.K., Rajagopalan, S., Singh, J. et al. Low-Frequency Adaptation-Deep Neural Network-Based Domain Adaptation Approach for Shaft Imbalance Fault Diagnosis. J. Vib. Eng. Technol. 12, 375–394 (2024). https://doi.org/10.1007/s42417-022-00848-7

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