Railway Fastening System Basing Condition Detection

Railway fastening system is an important part of a high-speed railway track. It is always critical to the operational safety and comfort of railway vehicles. Therefore, the condition detection of the railway fastening system, looseness or absence, is an important task in railway maintenance. However, the vision-based method cannot identify the severity of rail fastener looseness. With the development of railway technology, the condition of railway fastening system is monitored based on an automatic and remote-sensing measurement system. Meanwhile, wavelet packet analysis is used to analyze the acceleration signals, based on which two damage indices are developed to locate the damage position and evaluate the severity of rail fasteners looseness, respectively. To verify the effectiveness of the proposed method, an experiment is performed on a high-speed railway experimental platform. The experimental results show that the proposed method is effective to assess the condition of the railway fastening system. The monitoring system significantly reduces the inspection time and increases the efficiency of maintenance management. Railway fastening systems are used for fixing the rail on the track slab and preventing the longitudinal and lateral movements of the steel rail. Meanwhile, through offering flexibility, the railway fastening system can also counteract the vertical wave movements of the track, from which would result in the reduction of the dynamic forces between wheel and rail. The main components of a railway fastening system are metal rail clips, insulating polymer plate, and track bolts. The components of a railway fastening system are vulnerable to fatigue damage due to wheel-rail interaction. The reduction of frictional resistance between the T-bolt and nut causes the clamping force of a rail fastener to decrease. In this situation, the rail longitudinal resistance will not meet the design requirements. The loosening of the nut gives rise to intensifying the track irregularity, increasing the interaction between the rail and wheel, and may cause train derailment. Therefore, the condition detection of railway fastening systems, for looseness or absence, urgently needs to be studied. The current literature provides a few image-based methods for detecting rail fastener defects. The methods mentioned above can detect the absence of fasteners effectively, but cannot identify the severity of rail fastener looseness. Structural damage identification based on vibration data has broad application prospects in structural health monitoring (SHM). Wang et al. presented a method to identify the looseness of rail fastener, in which the self-power density method was used to analyze the vibration signals from four acceleration sensors installed on the railhead. The results revealed that the power spectral density was significantly decreased in the frequency of 2.95 kHz when the rail fastener loosened. Ren et al. used quaternion-based three-channel joint transmissibility to detect the state of rail fasteners between two testing points. Their experimental results showed that the state indicator can identify the number of loosened fasteners. A method based on the combination of orthogonal empirical mode decomposition and the theory of time-frequency entropy was used to detect rail fastener state. Their experimental results showed that this method can identify the severity of rail fasteners looseness to some extent, but the damage location is not considered. Additionally, the defects may not be spotted in time. Therefore, an automatic and remote-sensing measurement system used to monitor the condition of rail fasteners is always desirable.

Recently, the damage identification using structural dynamic responses based on time-frequency analysis has received growing attention in the field of railway structural health monitoring. Oregui et al. used a vehicle-borne monitoring system to detect and assess the tightness condition of bolts at rail joints based on wavelet analysis. The theoretical research showed that a series of component signals can indicate the dynamic properties of the structural system in various frequency bands after performing wavelet packet transform (WPT). Therefore, the presence of structural damage can be detected by the wavelet packet energy spectrum.

This paper presents an efficient method to locate the position and evaluate the severity of nut looseness in real-time based on a remote measurement system. The damage location and severity are investigated by the new approach based on the combination of the wavelet packet energy spectrum and statistical theory. An experiment is performed on a high-speed railway experimental platform to test the proposed method.

Monitoring System for Railway Fastening System

A remote measurement system is proposed to monitor the state of rail tracks by the Dalian University of Technology. As Figure 2 shows, the system consists of data acquisition nodes, gateways, base stations, an access server, a database client, and a monitoring system client. The data acquisition node collects vibration acceleration data using a uniaxial piezoresistive acceleration sensor which is incorporated into the fixture. The data are uploaded to gateway via a Zigbee network. The base station communicates with gateway using a 3G network. The access server obtains data from the Internet and stores the data into a database. Users can access the database through Web service technology to obtain data. The solar power technology is used to prolong the lifetime of the monitoring system. According to different functions, the data acquisition node (see Figure 3) is composed of a power circuit board, a signal conditioning circuit board, and a wireless communication circuit board. The power circuit board is designed to provide a stable working voltage for the other two boards. The vibration signals with high sampling rate are temporarily stored in an external large-capacity NAND FLASH after conditioning, filtering, and ADC discrete sampling by the signal conditioning circuit board. The data are uploaded to a gateway by a communication circuit board via a Zigbee network after sampling is finished.