# On the Track to Application Architectures in Public Transport Service Companies
## 4 Current State of Use-Cases from Different Transportation Service Providers
### Predictive Maintenance of the Pantograph-Catenary System at RBS  
Regionalverkehr Bern-Solothurn (RBS) is a Swiss public transport company in the region of Bern and Solothurn. RBS operates a fleet of 50 trains and 48 buses, a train network of 54 kilometers and the corresponding infrastructure. Over 86,000 passengers using RBS' transport services on peak days. RBS is part of the S-train network in the city of Bern and has a very dense and optimized timetable. Therefore, it is important to prevent incidents as efficient as possible. However, faulty infrastructure or rolling stock can cause delays, and even minor disturbances could provoke major disruption to the entire timetable. There are many causes for delays, some of them can be predicted and some of them cannot. Nevertheless, periodic maintenance can help to minimize the risk of delays and accidents. However, they are time-consuming and cause idle times. Furthermore, it is important to know what the source and root cause of the defects are. This allows to determine appropriate maintenance activities. Today, with the help of sensors, it is possible to collect plenty of data. However, processing and detecting anomalies in the collected data is too complex and time-consuming for a human to perform. In this use case, machine learning could help to minimize this effort. In order to localize arc ignitions in the pantograph-catenary system during train operation, which is an anomaly, no automatic detection system can be drew on today. The time of the occurrence, the location, the acceleration of the train or further context data would be interesting to know in order to determine why arcs occur and where the catenary system should be inspected. Figure 4: Simulated boarding and deboarding scenarios were performed to measure the accuracy of the newly developed AI as well as the infrared passenger counting system. To address these challenges the arcVision System prototype was developed in the context of a master thesis at FHNW in corporation with RBS [20]. The system consists of two different parts. The first part is the arcVision Scanner, which is able to collect the necessary sensor and context data, consisting of a single board computer, a GPS receiver, a camera, a temperature and humidity sensor. The second part is the arcVision Model, a Convolutional Neural Network (CNN) used for arc detection of the recorded pictures from the arcVision Scanner. The goal of the prototype was to achieve a TRL6, where the prototype needs be demonstrated in the operational environment and had to be mounted on the roof of a train in order to collect data. A supervised ML approach was used to train the ArcVision Model, which is a CNN based on Keras TensorFlow. As visualized in the Hybrid-AI Engineering lane of the AI-SDLC in Figure 2, the prototype is developed in the sub-process Machine Learning and optimized iteratively. The amount of time for the design, implementation and testing of the TRL3 prototype was quite reasonable and the return on investment of an AI-based solution was feasible even with limited resources and prior knowledge about machine learning. In order to collect all the necessary data, the arcVision Scanner was integrated into the operational environment respectively into the regular daily time schedule for a few days. Fortunately, the recorded data was viable for the validation and testing of the arcVision Model. Eventually, the arcVision System had an adequate accuracy in arc ignition detection and TRL6 could be achieved. However, the model has still some problems with false positives and predicts arc ignitions, which did not exist on the image. Figure 5 shows various characteristics of arc ignitions, which were part of the data-set and provided valuable insights about the arc ignition patterns, which were out of reach before.