Structural Health Monitoring in the Railway Industry: A Review

Abstract

Wayside detection monitors critical parameters relating to the condition of in-service railway vehicles. Economic decisions about the maintenance of vehicles can be made, and servicing can occur when a particular vehicle is likely to cause even small amounts of damage to the track, to itself, or when the cost of damage is significant, such as in catastrophic failure. Vehicles with poorly performing axle bearings, out-of-round (skidded or spalled) wheels, vehicles which exhibit transient lateral motion (‘hunting’), and vehicles with poorly performing brakes are all likely to fall into the category of requiring maintenance, in order to save the track and the vehicle owner's money. In the present paper, the parameters that define vehicle condition and their measurable effects are stated. There are frequently a number of wayside detection methods of inspecting a vehicle for the same vehicle condition and each of these is described in detail. This investigation reveals the need for further research to enable rollingstock owners to make better decisions about the cost of operating their vehicles, based on the output from wayside detectors and the observed trends in wheel impact.

Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside

“…In condition monitoring of railway vehicles, three main categories are existed; model based dynamical techniques including various Kalman [1,2] and particle filter [3], signal based techniques including band-pass filter, spectral analysis, wavelet analysis and Fast Fourier Transform [4], and wayside sensor based approaches which are generally practical in wheel defects according to a recent review [5]; using accelerometer and piezoelectric sensors on rail and using wavelet based methods and thresholding to identify the degree of the wheel flat [6], using a high speed camera to identify the wheel profile when the railway vehicle is passing by a low speed (10 mph) and image analysis, using optical sensors, accelerometers, load cells and strain gages to measure vertical deflection of the rail which makes it able to identify wheel defects like; out-of round, flat, shelling, measuring lateral force to determine bogie performance using hunting track detectors [7], acoustic bearing defect detectors which is based on statistical processing of the data, ultrasonic cracked wheel detection and many others [8].…”

Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high-frequency acoustic emission signals

“…The use of WPDs eliminates the need for manual profile inspection. 9 Both WILDs and WPDs have proven their reliability in the field already. However, these techniques are not capable of monitoring railway axle bearings.…”

State-of-the-Art Review of Railway Track Resilience Monitoring

“…The data are converted into the loads applied at the rail head by assuming a linear elastic response under certain measurement conditions. In addition, the strain gauge is also applied for capturing lateral loads on the rail, thus enabling the quantification of lateral to vertical load ratio for train derailment early warning [55][56][57].…”