Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Abstract

Wheel flats are amongst the most common local surface defect in railway wheels, which can result in repetitive high wheel-rail contact forces and thus lead to rapid deterioration and possible failure of wheels and rails if not detected at an early stage. The timely and accurate detection of wheel flats is of great significance to ensure the safety of train operation and reduce maintenance costs. In recent years, with the increase of train speed and load capacity, wheel flat detection is facing greater challenges. This paper focuses on the review of wheel flat detection techniques and flat signal processing methods based on wayside deployment in recent years. Commonly used wheel flat detection methods, including sound-based methods, image-based methods, and stress-based methods are introduced and summarized. The advantages and disadvantages of these methods are discussed and concluded. In addition, the flat signal processing methods corresponding to different wheel flat detection techniques are also summarized and discussed. According to the review, we believe that the development direction of the wheel flat detection system is gradually moving towards device simplification, multi-sensor fusion, high algorithm accuracy, and operational intelligence. With continuous development of machine learning algorithms and constant perfection of railway databases, wheel flat detection based on machine learning algorithms will be the development trend in the future.

Keywords: signal processing method; wayside signal acquisition method; wheel flat detection.

Figure 1

(a) Total railway passenger volume in China in 2021. (b) Total railway freight turnover in China in 2021.

Figure 2

Main categories of railway failures in the United States from 2019 to 2022.

Figure 3

Wayside in-service flat detection system schematic.

Figure 4

A view of the measurement points locations with a basic designation of dimensions and measurement realization. ω0: rotational velocity of the wheel, PM1~PM4: points of measurements [70].

Figure 5

A schematic diagram of the parallelogram mechanism [73].

Figure 6

Plans for sensor arrangement [77].

Figure 7

The schematic diagram of the sensor and the rail vertical deformation: (a) The installation location of the sensor; (b) The schematic diagram of the sensor; (c) The rail vertical deformation [78].

Figure 8

Strain gauges’ positions [80].

Figure 9

The scheme of measuring positions in the pass-by test; M—Microphones, P—photocells [89].

Figure 10

The scheme of measuring positions during acoustic pass-by tests; M1–M3—Microphones, P—Photocells [90].

Figure 11

Usage statistics for different sensors.