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Experimental Evidences in Bearing Diagnostics for Traction System of High Speed Trains
No full textRolling element bearings are the most critical components in the traction system of high speed trains.
Monitoring their integrity is a fundamental operation in order to avoid catastrophic failures and to
implement effective condition based maintenance strategies. Generally, diagnostics of rolling element
bearings is usually performed by analyzing vibration signals measured by accelerometers placed in the
proximity of the bearing under investigation. Several papers have been published on this subject in the last
two decades, mainly devoted to the development and assessment of signal processing techniques for
diagnostics. The experimental validation of such techniques has been traditionally performed by means of
laboratory tests on artificially damaged bearings, while their actual effectiveness in specific industrial
applications, particularly in rail industry, remains scarcely investigated. This paper is aimed at filling this
knowledge gap, by addressing the diagnostics of bearings taken from the service after a long term
operation on the traction system of a high speed train. Moreover, in order to test the effectiveness of the
diagnostic procedures in the environmental conditions peculiar to the rail application, a specific test-rig has
been built, consisting of a complete full-scale train traction system, able to reproduce the effects of wheeltrack
interaction and bogie-wheelset dynamics. The results of the experimental campaign show that
suitable signal processing techniques are able to diagnose bearing failures even in this harsh and noisy
application. Moreover, the most suitable location of the sensors on the traction system is proposed, in
order to limit their number
Architecture of the monitoring system for the traction system bearings of a regional locomotive
No full textUsing support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications
Full text linkThis paper addresses the problem of estimating continuous boundaries between acceptable and unacceptable engineering design parameters in complex engineering applications. In particular, a procedure is proposed to reduce the computational cost of finding and representing the boundary. The proposed methodology combines a low-discrepancy sequence (Sobol) and a support vector machine (SVM) in an active learning procedure able to efficiently and accurately estimate the boundary surface. The paper describes the approach and methodological choices resulting in the desired level of boundary surface refinement and the new algorithm is applied to both two highly-nonlinear test functions and a real-world train stability design problem. It is expected that the new method will provide designers with a tool for the evaluation of the acceptability of designs, particularly for engineering systems whose behaviour can only be determined through complex simulations
Signal Processing Diagnostic Tool for Rolling Element Bearings Using EMD & MED
No full textThe signal processing techniques developed for the diagnostics of me-chanical components operating in stationary conditions are often not applicable or are affected by a loss of effectiveness when applied to signals measured in transi-ent conditions. In this paper, an original signal processing tool is developed ex-ploiting some data-adaptive techniques such as Empirical Mode Decomposition, Minimum Entropy Deconvolution and the analytical approach of the Hilbert trans-form. The tool has been developed to detect localized faults on bearings of traction systems of high speed trains and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on envelope analysis or spectral kurtosis, which represent until now the landmark for bearings diagnostics
Experimental Evidences in the Monitoring of Rolling Element Bearings
No full textThe identification of the damage type in rolling element bearings is usually performed by means of Envelope Analysis of
vibration signal. This method is based on the identification of bearing damage frequency components in the so-called Envelope
Spectrum. Conversely, the monitoring and the evaluation of the trend of a suitable fault indicator are complex tasks to be
performed. The fault indicator must be robust against variations of system operating conditions and external vibration sources
to avoid misleading results. In the paper, the case of a rolling element bearing in which the defect develops until a severe
failure is described as well as the algorithm implemented for alarm signaling
Polynomial tyre model based on MIMO correlation analysis and coherent output index of racecar telemetry data
No full textTuning Second Order Cyclostationarity Parameters for the Diagnosis of Rolling Element Bearings
No full textFailures on rolling element bearings usually originate from cracks that are detectable even in their early stage of propagation by properly analyzing vibration signals measured in the proximity of the bearing. Due to micro-slipping in the rollers-races contact, damage-induced vibration signals belong to the family of quasi-periodic signals with a strong 2nd order cyclostationary component. Cyclic coherence and its integrated form are widely considered as the most suitable tools for bearing fault diagnostics and their theoretical bases have been already consolidated. This paper presents how to correctly set the parameters of the cyclostationary analysis tool to be implemented in an automatable algorithm. In the first part of the paper some general guidelines are provided for the specific application. These considerations are further verified, applying cyclostationary tools to data collected in an experimental campaign on a specific test-rig
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