Hybrid Models and Digital Twins for Condition Monitoring: HVAC System for Railway

Abstract

Safety passenger transportation is more important than efficiency or reliability. Therefore, it is vital to maintain the proper condition of the equipment related to the passengers' comfort and safety. This manuscript presents the methodology of complete development and implementation of both hybrid model and digital twin 3.0 for an HVAC in railways. The objective of this is to monitor the condition of the HVAC where it matters to the comfort and safety of the passengers in the trains. The level 3.0 of digital twin will be developed for the diagnosis and prognosis of HVAC by using hybrid modeling. The description illustrated in this paper is focused on the methodology used to implement a hybrid model-based approach, and both the need and advantages of using hybrid model approaches instead of data-based approaches. The development considers the importance of safety and environmental risks, which are included in the risk quantification of failure modes. Railway's maintainers replace critical components in early stages of degradation; thus, the use of a data-driven model loses essential information related to advanced stages of degradation which might decrease the accuracy of the maintenance instructions provided. Physics-based model can be used to generate synthetic data to overcome the lack of data in advanced stages of degradation, and then, the synthetic data can be combined with the real data, which is collected by sensor located in the real system, to build the data-driven model. The combination leads to form hybrid-model based approach with a large number of failure modes that were unpredictable. Finally, the outcome is beneficial for the proper functioning of systems; hence, safety of the passengers.