Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS), Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129 Torino, Italy.
Sensors (Basel). 2022 Dec 25;23(1):211. doi: 10.3390/s23010211.
The training of Artificial Intelligence algorithms for machine diagnosis often requires a huge amount of data, which is scarcely available in industry. This work shows that convolutional networks pre-trained for audio classification already contain knowledge for classifying bearing vibrations, since both tasks share the need to extract features from spectrograms. Knowledge transfer is realized through transfer learning to identify localized defects in rolling element bearings. This technique provides a tool to transfer the knowledge embedded in neural networks pre-trained for fulfilling similar tasks to diagnostic scenarios, significantly limiting the amount of data needed for fine-tuning. The VGGish model was fine-tuned for the specific diagnostic task by handling vibration samples. Data were extracted from the test bench for medium-size bearings specially set up in the mechanical engineering laboratories of the Politecnico di Torino. The experiment involved three damage classes. Results show that the model pre-trained using sound spectrograms can be successfully employed for classifying the bearing state through vibration spectrograms. The effectiveness of the model is assessed through comparisons with the existing literature.
人工智能算法的训练通常需要大量的数据，而这些数据在工业界中却很少。本研究表明，经过音频分类预训练的卷积网络已经包含了对轴承振动进行分类的知识，因为这两个任务都需要从声谱图中提取特征。通过迁移学习实现知识迁移，以识别滚动轴承的局部缺陷。该技术提供了一种工具，可以将在执行类似任务的神经网络中预先嵌入的知识转移到诊断场景中，从而大大减少了微调所需的数据量。通过处理振动样本，对 VGGish 模型进行了特定诊断任务的微调。数据是从都灵理工大学机械工程实验室专门设置的中号轴承试验台上提取的。实验涉及三个损伤等级。结果表明，使用声音声谱图预训练的模型可以通过振动声谱图成功地用于对轴承状态进行分类。通过与现有文献的比较来评估模型的有效性。
