基于深度学习的含未知复合故障多传感器信号故障诊断

Fault diagnosis of multi-sensor signal with unknown composite fault based on deep learning

深度学习在故障诊断领域的应用已比较成熟,其中卷积神经网络(CNN, convolution neural networks)和长短时记忆网络(LSTM, long short-term memory networks)就是典型模型之一。CNN作为一种常用的多传感器信号故障诊断方法,能够获得较好的诊断效果,却无法实现未知复合故障的诊断,为解决这个问题,提出CNN-LSTM-FCM(fuzzy C-means)模型。LSTM对具有前后联系的时间信号更敏感,利用这个特点将LSTM与CNN相结合,实现未知信号的诊断,并通过概率分类输出实现了复合故障的解耦,CNN-LSTM-FCM模型本身优化参数设计,进一步提高了诊断精度。使用化学过程故障测量数据进行实验,结果表明CNN-LSTM-FCM模型诊断准确率可达到97.15%,优于CNN模型和LSTM模型,具有较高的应用价值。

Deep learning has been widely used in the field of fault diagnosis, among which convolution neural networks(CNN) and long short-term memory networks(LSTM) are typical models. As a common method of multi-sensor signal fault diagnosis, CNN can obtain good diagnosis effect, but it can not diagnose unknown composite fault. In order to solve this problem, a CNN-LSTM-FCM(fuzzy C-means) model was proposed. Since LSTM was more sensitive to the time signals with the connection between the front and the back, it was combined with CNN to realize the diagnosis of unknown signals. The decoupling of composite fault was achieved through the probability classification output. The CNN-LSTM-FCM model itself had optimized parameter design, which further improved the diagnosis accuracy. The chemical process fault measurement data was used for experiments, and the results showed that the diagnostic accuracy of CNN-LSTM-FCM model could reach 97.15%, which was superior to both CNN model and LSTM model, thus having a high application value in fault diagnosis.