# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 4 Examples of Accelerometers Used in Rail Vehicle Systems
This system can transmit wireless data, exhibits a high sampling rate, and employs IoT technology to reduce system costs. The MESH wireless intelligent network transmits data collected by edge node sensors to the central node and subsequently uploads these data to the cloud. The experimental test of the system took place in a subway, where the collection nodes were distributed across multiple locations within the rail vehicle compartment. The reliability of the encapsulated node collection was validated based on the data characteristics collected on-site. The detection of train arrivals at track maintenance sites is necessary to ensure worker safety. In [67], a new method for train detection is proposed using a sensor on the track. Using a well-designed system to collect data and a good method to process these data, we can detect a train approaching from about 1 km away from where the sensors are set up. Several tests were performed on real train tracks, and these tests showed that this approach is very reliable. A particular range of vibration frequencies has been identified as the most effective for tracking when a train is arriving. An alarm is triggered when the continuously measuring vibrations surpass a specific value. The article [68] presents the development and testing of a self-contained railway track monitoring system that fuses signals from a laser Doppler vibrometer (LDV), axle box accelerometers (ABAs), GPS, and a video camera for anomaly detection in railway tracks. The multi-signal-processing method is proposed to obtain train-track vibrations with train position and speed. Multiple signals are extracted from the fuse, including an impact index, a resonance index, and an interpretable anomaly detection strategy. Additionally, the system contains positioning, speed estimation, and speed normalization processes; thus, it is considered more self-contained. The normalization eliminates the speed-dependent characteristics of the power spectral density (PSD), providing adaptability to varying train speeds. Technologies for detecting track irregularities are crucial for ensuring the safety and comfort of high-speed trains. One innovative method involves onboard equipment that uses a special algorithm to identify track issues [69]. This system monitors the railway condition and assists with timely maintenance. The setup includes devices for data collection, synchronization, and processing. By analyzing vibrations from the train's axle box, the algorithm effectively identifies irregularities in the track's alignment. This technology has been tested on various high-speed rail lines, proving to be highly accurate. Overall, it helps maintain smooth and safe train operations by detecting potential track problems early on. In [70], the text discusses a method used to understand how trains interact with railway tracks at crossings. This approach combines measurements taken directly from train axle boxes with tests using accelerometers to evaluate how different factors affect the train's behavior. These factors include the speed of the train, the direction in which it is moving (whether it is approaching or leaving), where the sensors are placed (either on the front or back wheels of a train car), and how the track itself responds naturally. Railway transport safety is directly connected to the condition of railway tracks and wheel surfaces. Irregularities of a railway track lead to various defects in the railway; thus, it is necessary to estimate the length of short and impulse irregularities. In addition, conducting a joint analysis of vibration acceleration signals recorded by accelerometers to study the types and sizes of railway track irregularities is essential. Components containing information about the railway track bogie detected oscillations were concentrated in the low-frequency domain. The paper [71] presents several algorithms based on the continuous wavelet transform.