# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 4 Examples of Accelerometers Used in Rail Vehicle Systems
The proposed procedure is based on measurements obtained from six single-axis accelerometers mounted on the axle box of the tra  
Figure 13: Model of the vehicle–track multi-body dynamic system—reprinted from [60]. The rail irregularity is obtained by the frequency-domain analysis of the measured signals. The practical testing was performed by equipping a rail vehicle with the proposed system and collecting data during a running day. The system was used to estimate the power spectrum of the rail irregularity in terms of roughness level on a track section of a subway line between two stations. The results indicate the corrugation on the low rail of both curves in the 100-300 mm wavelength band, which was confirmed by in-field observations. The proposed methodology can thus be used to continuously monitor rail corrugation at service speeds. Another example of acceleration information used in railway track monitoring is in the paper [63]. MEMS sensors located on the bogie and car body provide valuable insights into track alignment. These data are further analyzed through a mathematical model and frequency response analysis. Both simulation and field results demonstrate the efficacy of the proposed system. To optimize the irregularity values obtained from accelerometers installed in the axle box and bogie, the differential evolution (DE) algorithm is employed. Simulations are conducted at two different train speeds, allowing for a comparison between traditional track geometry measurements and the new DE method. The findings highlight the effectiveness of this innovative approach. In the UK, the Rail Safety and Standards Board (RSSB) funded a study [64] to create a cost-effective solution for the detection of track voids and other track defects using an in-service multi-train system. This study involved the development of an algorithm based on a state machine design methodology, which was subsequently tested on experimental data. The system consists of MEMS sensors that measure the acceleration of the rail vehicles, and FFT analysis is used to detect the RMS values of the vibration frequency spectrum. The authors claim that the results show a good agreement with reported track faults; thus, the system can provide a low-cost recorder of rail track defects. The study in [65] presents an algorithm to detect hunting oscillations in railway vehicles using accelerometer sensors. According to the authors, there are inherent hunting  
Figure 14: Experimental setup for wheel assessment—reprinted from [61]. Figure 15: Accelerometers on the axle box of a trailer bogie: (**a**) side A, vertical axle box; (**b**) side B, vertical and lateral axle box—reprinted from [62]. oscillations because the wheel's profile is a conical shape. These oscillations appear at different speeds, which are determined by the wheel profile, rail profile, and the vehicle's dynamics. These oscillations are a source of discomfort and instability during riding and can increase the risk of derailment. The algorithm in the paper is based on the lateral and longitudinal accelerations of the railway bogie, and data are obtained by mounting three-axis accelerometers above the axle boxes. The data are used to calculate an Index of Hunting Oscillation. Simulations were carried out using the SIMPACK 2024 software program to validate their algorithm, while the practical validation was conducted on data acquired from actual bogie dynamic tests. The results indicate a detection accuracy of 85%. This result is lower than the average accuracy of other methods; however, the complexity and computational effort of the proposed method are lower than those of different methods. The authors plan to improve the performance of the proposed algorithm. The main constraints of conventional rail vehicle vibration monitoring systems pertain to their substantial volume, intricate wiring configuration, and restricted operational range. In the paper [66], a PHM (Prognostics Health Management) vibration monitoring system for rail vehicles is proposed based on the Internet of things (IoT).