Reconstruction of an informative railway wheel defect signal from wheel-rail contact signals measured by multiple wayside sensors

Abstract

Wheel impact load detectors are widespread railway systems used for measuring the wheel-rail contact force. They usually measure the rail strain and convert it to force in order to detect high impact forces and corresponding detrimental wheels. The measured strain signal can also be used to identify the defect type and its severity. The strain sensors have a limited effective zone that leads to partial observation from the wheels. Therefore, wheel impact load detectors exploit multiple sensors to collect samples from different portions of the wheels. The discrete measurement by multiple sensors provides the magnitude of the force; however, it does not provide the much richer variation pattern of the contact force signal. Therefore, this paper proposes a fusion method to associate the collected samples to their positions over the wheel circumferential coordinate. This process reconstructs an informative signal from the discrete samples collected by multiple sensors. To validate the proposed method, the multiple sensors have been simulated by an ad hoc multibody dynamic software (VI-Rail), and the outputs have been fed to the fusion model. The reconstructed signal represents the contact force and consequently the wheel defect. The obtained results demonstrate considerable similarity between the contact force and the reconstructed defect signal that can be used for further defect identification.

Keywords: Railway; condition monitoring; contact; defect; signal reconstruction; wheel.

Figure 1.

The configuration of the wheel, rail, sleepers, and sensors for (a) a uniform track structure with joined sensors, (b) the typical rail–sleeper structure with joined sensors, (c) the typical structure with discrete sensors on the sleepers, and (d) the typical structure with discrete sensors between the sleepers.

Figure 2.

(a) The vertical wheel–rail contact forces measured in a sleeper bay, (b) rail bending moment above the sleeper, and (c) rail bending moment above another sleeper with a distance from the prior sensor.

Figure 3.

The schematic view of (a) the defect signal g(t), (b) the wheel signal w(t), (c) the windowed defect signal $g_1(t)$, and (d) the measured signal $z_1(t)$.

Figure 4.

(a) The configuration of the wheel, rail, sleepers, and sensors; (b) the defect signal; (c) inactive, transient, and effective zones of a sensor; and (d) the multiple sensors that collect multiple samples in their effective zone.

Figure 5.

The illustration of the fusion process.

Figure 6.

The process of the validation test.

Figure 7.

(a) The rail to sleeper displacement signal for the passage of four wheels while the first wheel is defective. This signal is considered as the measured signal z(t) and (b) The rail to sleeper displacement signal for the consecutive sleeper as the second sensor.

Figure 8.

(a) The simulated data sampled by 59 sensors using the MSM and (b) the magnified view of the plot (a).

Figure 9.

(a) The simulation result of a wheel–rail contact force for a wheel with 40 mm flat and 30 m/s velocity. The signals reconstructed from the rail to sleeper displacement signal collected by 59 sensors using (b) the SSM and (c) the MSM.

Figure 10.

The signals reconstructed for the wheels with different defects: (a) healthy wheel, (b) 60 mm wheel flat, (c) third-order out-of-round wheel, and (d) 40 mm wheel flat. The signals have been normalized by subtracting the average of the signals.