关键词: 故障检测, 特征提取, 长短时记忆神经, 在线更新

Abstract: In the era of industry 4.0, with the wide application of IoT, fault detection of industrial has great significance to improve the reliability of the equipment. In the real industry scene, it’s difficult to use an unchangeable model to predict the status of equipment, because the relationships among devices are complex and changeable during the running time. In recent years, with the prevalent development of deep learning, it has become a mainstream for fault detection. This paper presents an online fault detection model based on Long Short-Term Memory（LSTM） neural network. A curve-registration method of correlation maximization algorithm is used to feature extraction for multi sensors. Then the paper applies the LSTM neural network to develop a fault detection model, and realizes the online detection and update of the model with the help of sliding window technology. The effectiveness of the proposed model is demonstrated by examining real cases in a power plant. The experimental results show the effectiveness of the proposed method.

Key words: fault detection, feature extraction, Long Short-Term Memory（LSTM） neural network, online update