Abstract
An efficient maintenance is a key consideration in systems of railway transport, especially in high-speed trains, in order to avoid accidents with catastrophic consequences. In this sense, having a method that allows for the early detection of defects in critical elements, such as the bogie mechanical components, is a crucial for increasing the availability of rolling stock and reducing maintenance costs. The main contribution of this work is the proposal of a methodology that, based on classical signal processing techniques, provides a set of parameters for the fast identification of the operating state of a critical mechanical system. With this methodology, the vibratory behaviour of a very complex mechanical system is characterised, through variable inputs, which will allow for the detection of possible changes in the mechanical elements. This methodology is applied to a real high-speed train in commercial service, with the aim of studying the vibratory behaviour of the train (specifically, the bogie) before and after a maintenance operation. The results obtained with this methodology demonstrated the usefulness of the new procedure and allowed for the disclosure of reductions between 15% and 45% in the spectral power of selected Intrinsic Mode Functions (IMFs) after the maintenance operation.
Highlights
Railway researchers, manufacturers, and operators consider that ‘the economic efficiency and competitiveness of railway transport depends on the safety, availability and maintenance’ of each structural element that suffers high stress, as bogies or wheelsets [1]
The inner loop is known as the sifting process. This loop runs until the extracted signal meets the Intrinsic Mode Functions (IMFs) conditions [32]: (1) In the whole data set, the number of extrema and the number of zero-crossings must either equal or differ at most by one; and (2) At any point, the mean value of the envelope defined by the local maxima, and the envelope defined by the local minima is zero
In which ∆t is the sample time, N is the number of data points in the signal, Xck (f ) is the Fourier transform of the IMF ck (t), and Sck is the power spectral density of IMF ck (t)
Summary
Manufacturers, and operators consider that ‘the economic efficiency and competitiveness of railway transport depends on the safety, availability and maintenance’ of each structural element that suffers high stress, as bogies or wheelsets [1]. Vibration analysis is one of the most used techniques for inspecting railway mechanical components in operation, as it allows for the conduction of tests on a wide range of elements, including railway infrastructure and rolling-stock [7]. Some authors apply vibration analysis to the rolling elements with the aim of inferring a change in the natural vibration modes [20] or a vibration pattern associated with known defects [21] In this framework, real scale cracked axles were tested under different load conditions and crack sizes [4,22], as well as complete bogies of a Shinkansen train with deliberately generated defects [23]. Studied the behaviour of bogie components by analysing their vibration signature after maintenance These tests were carried out using an underground train at low speed in depot-based test conditions.
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