Air Leakage Detection of Pneumatic Train Door Subsystems Using Open Set Recognition

For pneumatically operated train doors, continuous monitoring and earlier detection of air leakages would greatly reduce train downtime and ensure both safety and high availability of the rail system. Deep learning, a powerful tool to extract useful information from a large amount of sensor data is widely used in fault detection. A large collection of known fault samples is necessary to generate reliable deep learning models. However, in real-world applications, it is very difficult to precollect all possible types of fault or anomaly conditions to train the model. When an unknown sample is fed into a traditional deep learning model, the model may make misclassification and recognize this sample as one of the known classes. To address this issue, the concept of OSR was proposed, whose goal is not only to classify known classes but also to detect unknown samples. Existing OSR methods are commonly based on complex generative models, and the testing phase is divided into multiple stages, which makes it difficult to apply these OSR methods to real-time industrial tasks. In this article, we proposed an end-to-end OSR method with less computational cost based on a streamlined and lightweight convolutional neural network. The proposed method is applied to detect different levels of air leakage on the pneumatic train door subsystem. Compared with other existing OSR methods, the proposed method dramatically shortens the running time and reduces the use of parameters in the model with negligible loss of OSR accuracy.