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<p>In a noisy industry environment, to predict machine faults using vibration signals, a specially designed Deep Convolution Neural Network (DCNN) with an additional noisy layer has been recently demonstrated. On the contrary, this paper presents a noise-susceptible fault classification using frequency spectrums in standard DCNN. The study involves two types of spectrum representation (a) Short-time Fourier Transform (STFT)of raw original signal and (b) Hilbert Huang Transform (HHT) of Empirical mode decomposition-intrinsic mode function (EMD-IMFs) and two different datasets (i) CWRU Bearing dataset and (ii) Nasa Milling Dataset which is non-stationary. Three binary DCNN classification problems are performed. For bearing data both representations maintained 100% classification accuracies for noise range up to 10&#xa0;dB. HHT-EMD-IMF performs better for the non-stationary milling dataset. HHT-EMD-IMF is extended to bearing data set multiclass fault classification problem. The performance is compared with state-of-the-art work. The study compares all IMFs of clean and noisy signals to quantify the impact of noise on EMD for 8 different specific faults of the CWRU bearing dataset. The analysis of average normalized noise shows that EMD-IMF<sub>0</sub> has minimum deviation due to noise for all noise ranges. The DCNN model maintains 100% classification accuracy at high noise levels up to 10&#xa0;dB and is better than the state-of-the-art noisy CNN approach. An ablation study shows that the proposed method is highly susceptible to impulse noise as well. It is also shown that the proposed method does not need additional computation time for training as in noisy layer CNN.</p>

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Predictive machine health monitoring using deep convolution neural network for noisy vibration signal of rotating machine using empirical mode decomposition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月21日

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  • R. Pavithra,
  • Prakash Ramachandran

摘要

In a noisy industry environment, to predict machine faults using vibration signals, a specially designed Deep Convolution Neural Network (DCNN) with an additional noisy layer has been recently demonstrated. On the contrary, this paper presents a noise-susceptible fault classification using frequency spectrums in standard DCNN. The study involves two types of spectrum representation (a) Short-time Fourier Transform (STFT)of raw original signal and (b) Hilbert Huang Transform (HHT) of Empirical mode decomposition-intrinsic mode function (EMD-IMFs) and two different datasets (i) CWRU Bearing dataset and (ii) Nasa Milling Dataset which is non-stationary. Three binary DCNN classification problems are performed. For bearing data both representations maintained 100% classification accuracies for noise range up to 10 dB. HHT-EMD-IMF performs better for the non-stationary milling dataset. HHT-EMD-IMF is extended to bearing data set multiclass fault classification problem. The performance is compared with state-of-the-art work. The study compares all IMFs of clean and noisy signals to quantify the impact of noise on EMD for 8 different specific faults of the CWRU bearing dataset. The analysis of average normalized noise shows that EMD-IMF0 has minimum deviation due to noise for all noise ranges. The DCNN model maintains 100% classification accuracy at high noise levels up to 10 dB and is better than the state-of-the-art noisy CNN approach. An ablation study shows that the proposed method is highly susceptible to impulse noise as well. It is also shown that the proposed method does not need additional computation time for training as in noisy layer CNN.