Development of Bearing Fault Diagnosis Model Using Low Frequency Data Based on Knowledge Distillation

Abstract

Bearings are critical components for ensuring smooth rotational motion in mechanical systems, and reliable operation requires continuous condition monitoring for fault diagnosis. Recently, there has been growing interest in diagnosing bearing conditions using artificial intelligence, particularly deep learning-based approaches. However, in real industrial environments, limitations such as high sensor cost and restricted data storage often lead to the use of low sampling frequency sensor data, which poses challenges in developing accurate diagnosis models. To address this issue, this paper proposes a bearing fault diagnosis method based on knowledge distillation to enhance the utility of low sampling frequency data. High-frequency acceleration data were collected under both normal and faulty conditions and subsequently downsampled for knowledge distillation. A 1D CNN-based teacher model was trained using high-frequency data, and multiple loss functions were designed to distill both final predictions and intermediate features into a student model trained on low sampling frequency data. The performance comparison between models with and without knowledge distillation verified the effectiveness of the proposed approach. The results demonstrate the feasibility of developing fault diagnosis models using low sampling frequency data in real industrial settings and suggest an effective knowledge distillation strategy.