Abstract
Fault diagnosis is an important subfield of prognostic and health management (PHM). Intelligent fault diagnosis based on deep learning is the most popular data-driven method of the present. However, current researches are prone to ignoring the strong noisy backgroundin real working conditions and cannot achieve excellent performance in actual application. As we all know, noise reduction and feature extraction are two vital aspects in mechanicalfault diagnosis. In this article, an intelligent diagnostic model based onimproved stacked convolutional auto-encoder (ISCAE) and parallel attention-based convolutional blocks (PACB) is proposed. ISCAE-based module is constructed to reduce the noise of raw signals and then PACB-based module can synchronouslyextract local spatial feature and global feature automatically.To equalize the role of above-mentioned two modules which are serial in the proposed model, two modules are trained and optimized synchronously to simultaneously adjust the neural network weights. The capability and effectiveness of the model are evaluated using a dataset collected from real operating environment of main reducer. The comparative analysisresults show that the ISCAE-PACB-based model can reach the accuracy of 98.95% and is superior to existing models.
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The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
The researches were funded by the National Natural Science Foundation of China (Grant No. 62006028), the Natural Science Foundation of Hubei Province (Grant No.2023AFB909).
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Qing Ye carries out research and designedthe technical routes, completed the simulation experiments and implemented the main framework, wrote the thesis; Changhua Liu modified and proofread the article.
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Ye, Q., Liu, C. Intelligent fault diagnosis of automobile main reducer based onstacked convolutional auto-encoder and parallel attention-based convolutional blocks. Appl Intell 55, 24 (2025). https://doi.org/10.1007/s10489-024-05868-2
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DOI: https://doi.org/10.1007/s10489-024-05868-2