The design and implementation of instrumentation to collect real-time vibrational data from a quasi-steady state machine (a dry vacuum pump) for fault prediction diagnostics is presented. When simultaneous multiple data collection points are required on the same machinery, the use of conventional transducers such as piezoelectric accelerometers becomes impractical due to their price, as each needs an expensive associated charge amplifier. The use of inexpensive micromachined integrated micro-electrical-mechanical system accelerometers such as ADXL105 has been explored here as an alternative to piezoelectric accelerometers for obtaining reliable and predictable data for diagnostics. Surface micromachined accelerometers are a new technology and their usage for vibrational analysis has been conservative due to concerns over increased noise levels and tolerance to high temperatures. In this article, it is shown that such concerns can be allayed. The time and frequency domain vibration signatures obtained using both types of accelerometers are compared. The study shows that ADXL105 accelerometers can be an effective alternative low-cost high-quality solution for machine condition monitoring.

This paper presents an improved method of modelling the hysteresis effect in batteries using the discrete preisach model. The hysteresis effect can be seen as a path-dependent effect or history-dependent effect on the lithium ion battery. This effect complicates the relationship between State of Charge (SOC) and Open Circuit Voltage (OCV) as it is no longer simply a one to one relationship, but rather it is depending on history of the battery. To solve the aforementioned effect, a discrete preisach model is implemented in an improved battery model to encapsulate the SOC-OCV relationship.

A Morlet-like wavelet cluster-based method for band-pass filtering and envelope demodulation is described. Via appropriate choice of wavelet parameters, a wavelet cluster combined with multi-Morlet-like wavelets can be used as a band-pass filter with zero phase shift, flat topped pass-band and rapid attenuation in the transition band. It can be used to extract high frequency natural vibration components. The imaginary part of the Morlet-like wavelet cluster is the Hilbert transformation of its real part. This can be used to implement envelope demodulation and extract the envelope component of the high frequency resonance band. The method is applied for fault diagnosis relating to bearing defects in a dry vacuum pump. It is shown that the fault characteristic frequencies can be extracted effectively. The efficacy of the method is demonstrated in experimental studies.

A new scheme is proposed that combines Autoregressive (AR) modelling techniques and pole-related spectral decomposition for the study of incipient single-point bearing defects for a vibration based condition monitoring system. Vibration signals obtained from the ball bearings from the High Vacuum (HV) and Low Vacuum (LV) ends of a dry vacuum pump run in normal and faulty conditions are modelled as time-variant AR series. The appearance of spurious peaks in the frequency domain of the vibration signatures translates to the onset of defects in the rolling elements. As the extent of the defects worsens, the amplitudes of the characteristic defect frequencies' spectral peaks increase. This can be seen as the AR poles moving closer to the unit circle as the severity of the defects increase. The number of poles equals the AR model order.Although not all of the poles are of interest to the user. It is only the poles that have angular frequencies close to the characteristic bearing defect frequencies that are termed the 'critical poles' and are tracked for quantification of the main spectral peaks. The time varying distance, power and frequency components can be monitored by tracking the movement of critical poles. To test the efficacy of the scheme, the proposed method was applied to increasing frame sizes of vibration data captured from a pump in the laboratory. It was found that a sample size of 4000 samples per frame was sufficient for almost perfect detection and classification when the AR poles' distance from the centre of unit circle was used as the fault indicator. The power of the migratory poles was an alternative perfect classifier which can be used as a fault indicator. The analysis has been validated with actual data obtained from the pump. The proposed method has interesting potential applications in condition monitoring, diagnostic and prognostic-related systems.

This paper provides a practical rule for determining the minimum model order for Autoregressive (AR) based spectrum analysis of data from rotating machinery. The use of parametric methods for spectral estimation, though having superior frequency resolution than Fast Fourier Transform (FFT) based methods, has remained less favoured mainly because of the difficulties in estimating the model order. The minimum model order p min required is the ratio of the sampling rate and the rotating speed of the machine. This is the number of samples in one shaft revolution. Traditional model order selection criteria, Akaike Information Criterion (AIC), Finite Information Criterion (FPE), Minimum Description Length (MDL), Criterion Autoregressive Transfer-function (CAT), and Finite Information Criterion (FIC) are used to estimate the optimal order. These asymptotic criteria for model order estimation are functions of the prediction error and the optimal order of an AR model is chosen as the minimum of this function. Experimental results, using vibration data taken from a dry vacuum pump at different sampling rates and rotating speeds, show that at there is a marked reduction in the prediction error. p min For low speed rotating machinery, the optimal order is . As the speed of the rotating machine increases, p min there is some advantage in using twice or thrice , to produce more accurate frequency estimates. The Boxp min Jenkins method of order determination using autocorrelation and partial autocorrelations plots are also used for justification of the selection of this minimal order. † Member of the International Institute of Acoustics and Vibration (IIAV)(pp @-@)