Transfer Learning for Anomaly Detection Using Bearings' Vibration Signals

Predictive maintenance is becoming increasingly important in the industry. Despite considerable advances in data collection and data-driven models, there are still limitations when deploying models in practice. One of the main limitations is the large datasets required to train these models. As a potential solution, transfer learning can be used to reuse knowledge acquired from large datasets for similar tasks under different conditions. This paper investigates the transferability of the specific anomaly detection scenario, an unsupervised learning task with heavily imbalanced label distribution. This paper uses a dataset generated from a bearing test platform in which bearings are run until failure under different operating conditions. A lightweight deep learning model, MobileNetV2, is employed to create a baseline model capable of detecting anomalies for a specific working condition. The model is then adapted using transfer learning to identify anomalies under new operating conditions with limited data accurately. The results show that the data for new conditions is insufficient to train an adequate model and that transfer learning can overcome this limitation. The adapted models can detect anomalies before the expert's knowledge reference value. Although this shows that transfer learning can detect anomalies earlier, the results must be evaluated carefully to avoid false positives. While anomaly detection aims to identify changes in feature distributions, transfer learning aims to align different feature distributions. Transfer learning for unsupervised learning has rarely been explored. To the best of our knowledge, this is one of the few works addressing it in the context of predictive maintenance for anomaly detection.