Abstract
To achieve intelligent and effective fault diagnosis of motor bearings, a machine-learning-based approach is proposed in the paper. 1D-CNNs are adopted to extract the features and a softmax classifier is used to distinguish the faults. However, given that there are many kinds of faults in the complex system, and there is coupling between fault signals, the reliability of fault diagnosis based on single-dimension data is limited, therefore we take the current signals and vibration signals of the motor as the input simultaneously. What’s more, to achieve further improvement in fault diagnosis accuracy fault diagnosis and reduce computational effort, a self-attention layer is introduced after feature extraction to selectively strengthen the valid information of the features. Subsequently, the performance of the approach is demonstrated on the bearing dataset of KAt-DataCenter. Finally, the effectiveness of multi-dimension input and the superiority of the self-attention mechanism are verified through comparative experiments.
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Fang, L., Liu, Z., Jiang, D., Qu, R. (2023). Bearing Fault Diagnosis Using 1D-CNN Combined with Multi-Dimensional Input and Self-Attention Mechanism. In: Dong, X., Yang, Q., Ma, W. (eds) The proceedings of the 10th Frontier Academic Forum of Electrical Engineering (FAFEE2022). FAFEE 2022. Lecture Notes in Electrical Engineering, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-99-3408-9_73
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DOI: https://doi.org/10.1007/978-981-99-3408-9_73
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