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.

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.

Insulating BiFeO3 ceramics of single perovskite phase were prepared by rapid sintering using sol-gel derived fine powders. The ceramics are dense and consist of grains of 2–6μm in diameter. Their leakage current density remains lower than 3.02×10−4A∕cm2 under the poling field below 119kV∕cm. The main conduction mechanism from 15to119kV∕cm is space-charge-limited current relating to oxygen vacancies. The ceramics exhibit a saturated ferroelectric hysteresis loop with a large remanent polarization (2Pr=56μC∕cm2) under the applied field of 180kV∕cm. Weak ferromagnetism with remanent magnetization of 1.5×10−5μB∕Fe is observed at 10K.

This paper reports on fundamental research to investigate the influence of wheelset flexibility on the development of wheel polygonization of a locomotive. After preparing a flexible wheelset model by importing a FE (Finite Element) model into the MBS (Multi-Body System) environment, the investigation work proceeded in 3 steps. Firstly, FRF (Frequency Response Function) of the contact responses against the track irregularity is analysed for a free wheelset and an on-track wheelset, with consideration of rotation effect. Secondly, the influence of the wheelset flexibility on the contact responses excited by white noise is investigated for straight and curved tracks. The final step is to check the influence of the wheelset flexibility on the development of wheel polygonization based on a developed prediction program for railway wheel polygonization. 6 scenarios are investigated with comparison between rigid and flexible wheelsets. Results show that, the wheelset flexibility cannot dominate the railway wheel polygonization in a general sense, unless some prerequisites are fulfilled to provide a suitable environment for the wheelset flexibility to be effectively and continually excited in order to fluctuate the contact responses, and thereby initiate wheel polygonization. The torsional mode of the wheelset can be effectively excited by stick-slip vibration due to saturated contact adhesion that can occur on track with small curve radii or by large traction torque. In this case, the developed wheel polygonization order will be exactly determined by the wheelset torsional modal frequency and the vehicle speed.

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