YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency Fusion
This paper addresses the core contradiction in fault diagnosis of gearboxes in heavy-duty equipment, where it is challenging to achieve both lightweight and robustness in dynamic industrial environments. Current diagnostic algorithms often struggle with balancing computational efficiency and diagnos...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4862 |
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| author | Yihang Peng Jianjie Zhang Songpeng Liu Mingyang Zhang Yichen Guo |
| author_facet | Yihang Peng Jianjie Zhang Songpeng Liu Mingyang Zhang Yichen Guo |
| author_sort | Yihang Peng |
| collection | DOAJ |
| description | This paper addresses the core contradiction in fault diagnosis of gearboxes in heavy-duty equipment, where it is challenging to achieve both lightweight and robustness in dynamic industrial environments. Current diagnostic algorithms often struggle with balancing computational efficiency and diagnostic accuracy, particularly in noisy and variable operating conditions. Many existing methods either rely on complex architectures that are computationally expensive or oversimplified models that lack robustness to environmental interference. A novel, lightweight, and robust diagnostic network, YConvFormer, is proposed. Firstly, a time–frequency joint input channel is introduced, which integrates time-domain waveforms and frequency-domain spectrums at the input layer. It incorporates an Efficient Channel Attention mechanism with dynamic weighting to filter noise in specific frequency bands, suppressing high-frequency noise and enhancing the complementary relationship between time–frequency features. Secondly, an axial-enhanced broadcast attention mechanism is proposed. It models long-range temporal dependencies through spatial axial modeling, expanding the receptive field of shock features, while channel axial reinforcement strengthens the interaction of harmonics across frequency bands. This mechanism refines temporal modeling with minimal computation. Finally, the YConvFormer lightweight architecture is proposed, which combines shallow feature processing with global–local modeling, significantly reducing computational load. The experimental results on the XJTU and SEU gearbox datasets show that the proposed method improves the average accuracy by 6.55% and 19.58%, respectively, compared to the best baseline model, LiteFormer. |
| format | Article |
| id | doaj-art-b91d2ed699e5489f9db7afac421d0eea |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b91d2ed699e5489f9db7afac421d0eea2025-08-20T03:36:26ZengMDPI AGSensors1424-82202025-08-012515486210.3390/s25154862YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency FusionYihang Peng0Jianjie Zhang1Songpeng Liu2Mingyang Zhang3Yichen Guo4College of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaCollege of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaCollege of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaCollege of Software, Xinjiang University, Urumqi 830091, ChinaCollege of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaThis paper addresses the core contradiction in fault diagnosis of gearboxes in heavy-duty equipment, where it is challenging to achieve both lightweight and robustness in dynamic industrial environments. Current diagnostic algorithms often struggle with balancing computational efficiency and diagnostic accuracy, particularly in noisy and variable operating conditions. Many existing methods either rely on complex architectures that are computationally expensive or oversimplified models that lack robustness to environmental interference. A novel, lightweight, and robust diagnostic network, YConvFormer, is proposed. Firstly, a time–frequency joint input channel is introduced, which integrates time-domain waveforms and frequency-domain spectrums at the input layer. It incorporates an Efficient Channel Attention mechanism with dynamic weighting to filter noise in specific frequency bands, suppressing high-frequency noise and enhancing the complementary relationship between time–frequency features. Secondly, an axial-enhanced broadcast attention mechanism is proposed. It models long-range temporal dependencies through spatial axial modeling, expanding the receptive field of shock features, while channel axial reinforcement strengthens the interaction of harmonics across frequency bands. This mechanism refines temporal modeling with minimal computation. Finally, the YConvFormer lightweight architecture is proposed, which combines shallow feature processing with global–local modeling, significantly reducing computational load. The experimental results on the XJTU and SEU gearbox datasets show that the proposed method improves the average accuracy by 6.55% and 19.58%, respectively, compared to the best baseline model, LiteFormer.https://www.mdpi.com/1424-8220/25/15/4862gearbox fault diagnosistime–frequency fusionaxial decompositionlightweight modelnoise robustness |
| spellingShingle | Yihang Peng Jianjie Zhang Songpeng Liu Mingyang Zhang Yichen Guo YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency Fusion Sensors gearbox fault diagnosis time–frequency fusion axial decomposition lightweight model noise robustness |
| title | YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency Fusion |
| title_full | YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency Fusion |
| title_fullStr | YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency Fusion |
| title_full_unstemmed | YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency Fusion |
| title_short | YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency Fusion |
| title_sort | yconvformer a lightweight and robust transformer for gearbox fault diagnosis with time frequency fusion |
| topic | gearbox fault diagnosis time–frequency fusion axial decomposition lightweight model noise robustness |
| url | https://www.mdpi.com/1424-8220/25/15/4862 |
| work_keys_str_mv | AT yihangpeng yconvformeralightweightandrobusttransformerforgearboxfaultdiagnosiswithtimefrequencyfusion AT jianjiezhang yconvformeralightweightandrobusttransformerforgearboxfaultdiagnosiswithtimefrequencyfusion AT songpengliu yconvformeralightweightandrobusttransformerforgearboxfaultdiagnosiswithtimefrequencyfusion AT mingyangzhang yconvformeralightweightandrobusttransformerforgearboxfaultdiagnosiswithtimefrequencyfusion AT yichenguo yconvformeralightweightandrobusttransformerforgearboxfaultdiagnosiswithtimefrequencyfusion |