Neural Network for the Statistical Process Control of HVAC Systems in Passenger Rail Vehicles

In the rail industry, coach temperature regulation has become a crucial task to improve passenger thermal comfort. Over the past few years, European standards have required rail operators to implement monitoring systems for the control of heating, ventilation and air conditioning (HVAC) of passenger rail vehicles. These systems, based on modern automated sensing technologies, have created new data-rich scenarios and call for new methods to deal with high-dimensional, high-correlated and heterogeneous data. In this article, an autoencoder, which is a particular type of neural network developed to model unlabelled data and automatically extract significant features, is utilised to develop a nonparametric process monitoring approach. Two control charts based on statistics H2 and SPE are built in the feature space and the residual space, respectively. Through operational HVAC data collected on board passenger vehicles, the proposed approach is shown to be capable of simultaneously monitoring and detecting anomalies that may have occurred in the data streams acquired from each train coach, even though it is not limited to the application hereby investigated. Additionally, via a numerical investigation, the Phase II fault detection performance is compared with that of a simpler linear dimension reduction method and two more complex NN architectures.