The wheel-flat defect is one of the many issues decreasing the level of and comfort in light rail vehicles. The two main causes of wheel-flats are temporary or complete wheel blocking. The negative effect is the increase of the dynamic phenomenons in the wheel-rail interaction, which are impulsive in their nature. It could prove to be dangerous for the safety of the ride. Therefore, a regular wheel surface monitoring and wheel-flats detecting would be well-founded to the fast detection of a flat point and to take remedial measures. The main aim of the article is to present the algorithm, according to which the wheel-flat detection during tram passage is possible. Several vibration transducers were mounted on the rail and measured vibration amplitude during trams pass-by. The proposed method is based on the vibration signal processing in the time and frequency domain. The Hilbert transform was used in the algorithm and all research analyses. The carried out experimental study and the analysis of the results of the method, show a high efficiency in the wheel flat detection.
KeywordsTram, wheel-flat, Hilbert transform, envelope analysis Wheel-flat detection on trams using envelope analysis with Hilbert transform Tomasz Nowakowski et al.
One of the most important aspects in the operation of rail vehicles is the implementation of transport while ensuring safety and comfort of passengers. Technical condition of the wheel rolling surfaces has a direct impact on these factors. These elements are subject to wear and tear in a continuous and discrete form. One of the discrete wear forms is the wheel-flat on wheel rolling surfaces that generate impulse noise. This translates into a significant deterioration of vibroacoustic comfort, and in extreme cases also to a greater risk associated with e.g. derailment of the vehicle. Therefore, it is desirable in particular by the rolling stock operator to carry out cyclic diagnostics and monitoring of the condition of wheel rolling surfaces. This paper is a continuation in a series of research articles carried out by the authors related to vibroacoustic diagnostics of wheel rolling surfaces in light rail vehicles. As part of this article, acoustic measurements were carried out at a dedicated track-side system during so-called pass-by tests. Acoustic signals were analyzed in accordance with the Fourier and Hilbert transforms. Additionally, the main assumptions of vibroacoustic diagnostics and analyses of point measures were used. This allowed for the development of yet another way of monitoring the occurrence of the problem of wheel flats in rail vehicles.
Currently, one of the trends in the automotive industry is to make vehicles as autonomous
as possible. In particular, this concerns the implementation of complex and innovative selfdiagnostic systems for cars. This paper proposes a new diagnostic algorithm that evaluates the performance of the drive shaft bearings of a road vehicle during use. The diagnostic parameter was selected based on vibration measurements and machine learning analysis results. The analyses included the use of more than a dozen time domain features of vibration signal in different frequency ranges. Upper limit values and down limit values of the diagnostic parameter were determined, based on which the vehicle user will receive information about impending wear and total bearing damage. Additionally, statistical verification of the developed model and validation of the results were performed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.