# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 4 Examples of Accelerometers Used in Rail Vehicle Systems  
Accelerometers are being investigated as a possible solution for detecting above-normal vibrations experienced by railway cars or tracks. Continuous studies are carried out to improve detection methods and signal analysis algorithms, which provide information about the health of the rail tracks and rail cars that can be used for predictive and corrective maintenance, thus improving the safety and comfort of the railroad. In [58], the authors present a method for the early detection of a rail track defect called a squat caused by short wavelength rotational contact. Their method involves mounting three-axis accelerometers on the axle box, and the algorithm used to process the sensor data is based on a wavelet spectrum analysis. One of the findings of this paper was that accelerometer longitudinal measurements were more effective in detecting the squat defect than vertical measurements. The wavelet analysis identified the frequency band of the squat signals to be between 600 and 800 Hz, depending on the rail section, either joints, welds, or hanging sleepers. The proposed method was tested on a section of a high-speed line, and the results of the experiments showed a detection rate of 88%. The primary location where the squat defect was detected was at the rail welds and rail joint section. The system presented allows safety inspections to be performed at shorter intervals than regular inspections without using special inspection vehicles. The study [59] presents some issues when implementing MEMS accelerometers mounted on the sprung parts of the railcar bogie and the axle boxes. Different sensor positions and different sensor configurations have been investigated. The configuration of the sensors depends on the accuracy requirements, and it is suggested that sensors with different characteristics should be used to create a virtual accelerometer out of this mini-sensor network. Analyzing the signals requires algorithms such as the wavelet transform or the Fast Fourier Transform. The authors of [60] investigate the possibility of using an artificial intelligence algorithm to detect rail track defects based on accelerometer data mounted on running trains. Their method measures the axle box signals from eight onboard accelerometers, as shown in Figure 13.  
The AI algorithm uses an unsupervised learning approach to detect and track irregularities. The detection comprises four stages:  
* feature extraction using an autoregressive method;
* feature normalization;
* data fusion;
* damage detection. SIMPACK simulations with model parameters based on an authentic freight wagon were carried out. The results showed that the detection accuracy of the damaged longitudinal level, lateral alignment, cross-level, and twist is greater than or equal to 94%. The developed approach can be used for the early warning of passenger comfort and unbalanced loads affecting freight vehicles. A comprehensive study was performed in [61] regarding drive-by methodologies for collecting data on track irregularities, rail conditions, and rail supporting elements, including field experiments as shown in Figure 14. The authors conclude that drive-by methodologies have several promising future applications. These include track maintenance optimization, proactive fault detection, predictive maintenance, track performance evaluation, vehicle health monitoring, and data-driven decision-making. In [62], a procedure to monitor a rail track defect called rail corrugation is presented. This defect manifests as a quasi-periodic irregularity of the track's surface. It is a critical problem for urban railways because it induces ground vibrations in buildings near the rail track. Typical mitigation involves grinding the rail using a dedicated service vehicle, which is not the most efficient method, as it can only be performed when vehicles are not running on the tracks.