No abstract

The power spectrum is defined as the square of the magnitude of the Fourier transform (FT) of a signal. The advantage of FT analysis is that it allows the decomposition of a signal into individual periodic frequency components and establishes the relative intensity of each component. It is the most commonly used signal processing technique today. If the same principle is applied for the detection of periodicity components in a Fourier spectrum, the process is called the cepstrum analysis. Cepstrum analysis is a very useful tool for detection families of harmonics with uniform spacing or the families of sidebands commonly found in gearbox, bearing and engine vibration fault spectra. Higher order spectra (HOS) (also known as polyspectra) consist of higher order moment of spectra which are able to detect non-linear interactions between frequency components. For HOS, the most common used is the bisoectrum. The bispectrum is the third-order frequency domain measure, which contains information that standard power spectral analysis techniques cannot provide. It is well known that neural networks can represent complex non-linear relationships, and therefore they are extremely useful for fault identification and classification. This paper presents an application of power spectrum, cepstrum, bispectrum and neural network for fault pattern extraction of induction motors. The potential for using the power spectrum, cetstrum, bispectrum and neural network as means for differentiating between healthy and faulty induction motor operation is examined. A series of experiments are done and the advantages and disadvantages between them are discussed.

No abstract

This paper summarizes the forces that develop in the contact patch between the wheel and rail in a railway vehicle. The ways that these forces govern the behaviour of a vehicle running on straight and curved track are explained and the methods commonly used to calculate and utilize the forces summarized. As an illustration, the results from a computer simulation of a typical UK passenger train are presented and certain aspects examined.

Damage to the surface of railway wheels and rails commonly occurs in most railways. If not detected, it can result in rapid deterioration and possible failure of rolling stock and infrastructure components causing higher maintenance costs. This paper presents an investigation into the modelling and simulation of wheel flat and rail surface defects. A simplified mathematical model was developed and a series of experiments were carried out on a roller rig. Timefrequency analysis is a useful tool for identifying the content of a signal in the frequency domain without losing information about its time domain characteristics. Because of this it is widely used for dynamic system analysis and condition monitoring and has been used in this paper for the detection of wheel flats and rail surface defects. Three commonly used time-frequency analysis techniques: Short-Time-Fourier-Transform (STFT); Wigner-Ville Transform (WVT) and Wavelet Transform (WT) were investigated in this work.