Abstract: Efficient and accurate anomaly detection of power equipment is essential for aerospace safety. Scientific detection and maintenance can promptly identify potential faults and ensure the safety and reliability of the system. The data collected by sensors from power equipment contains valuable information. Feature extraction is usually required for processing these data. Although deep learning methods historically obtain excellent results, there is always a trade off between fine tuning existing networks or designing models from scratch for sensor data processing. To address this issue, we propose a temporal feature extraction network for time series data based on self supervised learning. First, we use self supervised learning methods to pre train the network. Then we devise a novel network model structure that can effectively extract the representation of time series data. Finally, we evaluate the proposed method on relevant datasets, and the experimental results demonstrate the effectiveness of the proposed method.

Key words: power equipment, anomaly detection, self supervised learning, series data