## 4 Industry 4.0 Concept in Railway Transportation
### Predictive Maintenance  
Big data is the most important term in today's digitalisation trend, and it denotes massive data sets [183] characterised by large volumes and different and complex structures, together with the requirement of complex analysis tools for the purpose of visualisation and the subsequent processing of data as input parameters for other systems. M2M data exchange, associated with smart sensors and IoT devices, and characterised by large data volumes, may be of particular interest when railway transport safety is concerned because of its utility in predictive maintenance in conjunction with proper classification and pattern recognition algorithms [184]. Predictive maintenance or condition-based maintenance recommends appropriate actions using a large set of collected data subjected to the processing and extraction of key features that are subsequently used for classification (normal operation, emerging fault, developed fault, and imminent failure) [185]. The classification result is subsequently used for timely maintenance decision making (Figure 12), thus reducing maintenance time and costs and avoiding unscheduled overhauls [185]. Sources of analysis data in railways can be wide and varied, such as (i) intelligent sensor networks on-board freight trains [114], (ii) optical sensors such as LIDAR (light detection and ranging) devices on-board trains, which are used for accident prevention [186], and (iii) cameras and other sensors aboard unmanned aerial vehicles (UAVs) used for infrastructure monitoring [187], just to name a few. Intelligent sensor networks on-board freight trains can be arranged depending on the type of transmission medium (wire, optics, or wireless) and type of data transmission, i.e., single-hop and multi-hop networks [114] (Figure 13). Naturally, wireless communication between sensor nodes and the data hub offers some distinct advantages over wire- or optics-based data links, particularly with respect to the lack of maintenance. In that respect, an electrical power supply for individual sensor nodes also plays a significant role, especially with freight wagons, which are typically unpowered, except for braking system pneumatics and the terminal signal light. Hence, such sensor nodes should either be equipped with a long-life battery or an energy harvesting system for on-board power supply [114]. One such case of autonomous sensors used for the online monitoring of the braking system pressure and mechanical vibrations by means of accelerometers, with a vibration energy harvesting system based on an inertial pendulum mounted on bogey suspension, was presented in [113]. The main conclusion in reference [113] was that the developed energy harvesting and ultracapacitor energy storage systems may need further miniaturisation and protection from environmental conditions before being deployable en masse. Other types of sensors that may be used for the online condition monitoring of railway vehicles include on-board inertial measurement units [188] and smart sensors within tunnels to facilitate an advanced risk assessment analysis [189]. Note that there are already commercially available on-board sensor systems that satisfy stringent requirements in terms of power autonomy (e.g., powered by means of solar panels) and high levels of robustness and ingress protection (IP class), thus allowing them to operate over a wide range of environmental conditions [190; 191] that are characteristic for rail freight transport. Figure 12: Principal representation of condition-based maintenance process [185]. Research in intelligent maintenance techniques in railways typically involves the utilisation of artificial intelligence, such as fuzzy logic and artificial neural networks (ANNs), and machine learning (ML) models in the form of support vector machines (SVMs) and support vector regression (SVR) models [184]. In that sense, the authors of [192] investigated a fuzzy logic-based thermography system for predictive maintenance in electric railway traction.