Transfer Learning with CLIP for Bearing Fault Diagnosis

Bearing fault diagnosis is critical for ensuring the proper maintenance of rotating machinery and avoiding catastrophic failures, especially in aerospace applications. Machine learning and deep learning-based models have shown promise for solving bearing fault diagnosis problems. Some major drawbacks with these models are: (1) They require a large amount of labeled data for training. (2) They do not provide good model generalization and cannot address the issue of versatility and variability. In recent years, a surge in the development and success of deep learning models such as GPT3 and Contrastive Language–Image Pretraining (CLIP), pre-trained on expansive datasets, has been observed across a multitude of applications. The emergence of these sophisticated pre-trained models has propelled transfer learning to the forefront as an immensely promising approach for tackling above mentioned issues. In this paper, a transfer learning approach for bearing fault diagnosis using a pre-trained CLIP model that combines image processing and natural language processing (NLP) is proposed. The effectiveness of the transfer learning method with CLIP is demonstrated using vibration data collected from plastic bearing seeded fault tests in the laboratory.