Predictive machine health monitoring using deep convolution neural network for noisy vibration signal of rotating machine using empirical mode decomposition
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| Foilsithe in: | SN Applied Sciences vol. 7, no. 4 (Apr 2025), p. 247 |
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| Foilsithe / Cruthaithe: |
Springer Nature B.V.
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| Ábhair: | |
| Rochtain ar líne: | Citation/Abstract Full Text Full Text - PDF |
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Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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MARC
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| 001 | 3180013618 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2523-3963 | ||
| 022 | |a 2523-3971 | ||
| 024 | 7 | |a 10.1007/s42452-025-06650-w |2 doi | |
| 035 | |a 3180013618 | ||
| 045 | 2 | |b d20250401 |b d20250430 | |
| 245 | 1 | |a Predictive machine health monitoring using deep convolution neural network for noisy vibration signal of rotating machine using empirical mode decomposition | |
| 260 | |b Springer Nature B.V. |c Apr 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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.Article Highlights<list list-type="bullet"><list-item></list-item>A novel way of using frequency spectrum of noisy vibration signal in a deep convolution neural network is proposed for machine fault classification of bearing data set and shown that it is faster to train and performs better than an existing noisy layered CNN method.<list-item>Two datasets are utilised a standard bearing data set with multiple faults and a milling dataset which is non stationary and two approaches binary class classification and multi-class classification are considered and experimented for two spectrum representations in which Hilbert transform of empirical mode decomposed intrinsic mode functions performed better for binary classification of noisy vibration signals even for additive white Gaussian noise range of 10 dB.</list-item><list-item>For multiclass classification that comprises of 8 faults, HHT of EMD-IMF0 performs better with 100% classification accuracy up to 10 dB additive Gaussian noise which is better than the existing noisy layered DCNN and in the propsed method the noisy training process consumes same time as clean signal training. The method is good as well for impulse noise for all noise ranges.</list-item> | |
| 653 | |a Hilbert transformation | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Classification | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Vibration monitoring | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Signal processing | ||
| 653 | |a Convolution | ||
| 653 | |a Ablation | ||
| 653 | |a Noise levels | ||
| 653 | |a Faults | ||
| 653 | |a Training | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Representations | ||
| 653 | |a Frequency spectrum | ||
| 653 | |a Machine learning | ||
| 653 | |a Vibration | ||
| 653 | |a Milling (machining) | ||
| 653 | |a Machinery | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Rotating machinery | ||
| 653 | |a Random noise | ||
| 653 | |a Decomposition | ||
| 653 | |a Rotating machines | ||
| 653 | |a Methods | ||
| 653 | |a Environmental | ||
| 773 | 0 | |t SN Applied Sciences |g vol. 7, no. 4 (Apr 2025), p. 247 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3180013618/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3180013618/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3180013618/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |