A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy Conditions
Rotating machinery is essential in modern industry. A robust noise-resistant method is proposed to improve diagnostic accuracy under intense noise conditions. Initially, time-domain signals are transformed into the time-frequency domain using the Synchrosqueezing Short-Time Fourier Transform to redu...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11014518/ |
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| author | Junyu Ren Soo Siang Teoh |
| author_facet | Junyu Ren Soo Siang Teoh |
| author_sort | Junyu Ren |
| collection | DOAJ |
| description | Rotating machinery is essential in modern industry. A robust noise-resistant method is proposed to improve diagnostic accuracy under intense noise conditions. Initially, time-domain signals are transformed into the time-frequency domain using the Synchrosqueezing Short-Time Fourier Transform to reduce the impact of noise. A novel hybrid adaptive fusion deep learning model is then introduced, incorporating two new modules: the heterogeneous convolution adaptive fusion block and the global-local attention adaptive fusion block. These modules enable the integration of heterogeneous convolution operators and complementary attention mechanisms, optimizing component importance for identifying subtle features despite noise. Additionally, the traditional Multilayer Perceptron in the classification layer is replaced with Kolmogorov-Arnold Networks to improve diagnostic accuracy. Case studies demonstrate that the method has strong noise resistance under challenging signal-to-noise ratio conditions. |
| format | Article |
| id | doaj-art-25b146992b0d4a0aa399dffb97ae92ac |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-25b146992b0d4a0aa399dffb97ae92ac2025-08-20T03:06:04ZengIEEEIEEE Access2169-35362025-01-0113914519146510.1109/ACCESS.2025.357290211014518A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy ConditionsJunyu Ren0https://orcid.org/0009-0004-8021-6641Soo Siang Teoh1https://orcid.org/0000-0002-3475-3735School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, MalaysiaSchool of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, MalaysiaRotating machinery is essential in modern industry. A robust noise-resistant method is proposed to improve diagnostic accuracy under intense noise conditions. Initially, time-domain signals are transformed into the time-frequency domain using the Synchrosqueezing Short-Time Fourier Transform to reduce the impact of noise. A novel hybrid adaptive fusion deep learning model is then introduced, incorporating two new modules: the heterogeneous convolution adaptive fusion block and the global-local attention adaptive fusion block. These modules enable the integration of heterogeneous convolution operators and complementary attention mechanisms, optimizing component importance for identifying subtle features despite noise. Additionally, the traditional Multilayer Perceptron in the classification layer is replaced with Kolmogorov-Arnold Networks to improve diagnostic accuracy. Case studies demonstrate that the method has strong noise resistance under challenging signal-to-noise ratio conditions.https://ieeexplore.ieee.org/document/11014518/Artificial intelligenceadditive noiseattention mechanismsconvolutional neural networksdata processingdeep learning |
| spellingShingle | Junyu Ren Soo Siang Teoh A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy Conditions IEEE Access Artificial intelligence additive noise attention mechanisms convolutional neural networks data processing deep learning |
| title | A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy Conditions |
| title_full | A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy Conditions |
| title_fullStr | A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy Conditions |
| title_full_unstemmed | A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy Conditions |
| title_short | A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy Conditions |
| title_sort | hybrid adaptive fusion deep learning model for fault diagnosis of rotating machinery under noisy conditions |
| topic | Artificial intelligence additive noise attention mechanisms convolutional neural networks data processing deep learning |
| url | https://ieeexplore.ieee.org/document/11014518/ |
| work_keys_str_mv | AT junyuren ahybridadaptivefusiondeeplearningmodelforfaultdiagnosisofrotatingmachineryundernoisyconditions AT soosiangteoh ahybridadaptivefusiondeeplearningmodelforfaultdiagnosisofrotatingmachineryundernoisyconditions AT junyuren hybridadaptivefusiondeeplearningmodelforfaultdiagnosisofrotatingmachineryundernoisyconditions AT soosiangteoh hybridadaptivefusiondeeplearningmodelforfaultdiagnosisofrotatingmachineryundernoisyconditions |