Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Cyclostationarity: half a century of research
Signal Processing
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This paper investigates the detection of a bearing defect in an asynchronous machine by analysing the electric signals. For this purpose, it is considered that the voltage is imposed and independent of mechanical aspect and that the mechanical defect appears only in the current thanks to the variation of impedance. Wiener filtering is used to extract mechanical information contained in the electrical current; this will then enable the use of statistical indicators such as kurtosis which identify the presence of a defect. Initially, the small fluctuation in electric current around the electric cycle (50Hz) is reduced in order to reinforce cyclostationarity. Then, a filter between the voltage and current is estimated, using Wiener's technique. Since the voltage is decorrelated of mechanical elements, the residual signal (current - predicted current) contains the mechanical part. This study is corroborated by an envelope analysis of the vibration signal. Experimentation on a faulty outer raceway bearing has shown the excellent performance of the proposed method. This method is easier to implement since the sensors' position does not influence the measure the way it does when using accelerometer sensors. This diagnosis could be embedded into a fed converter. However, it is less sensitive than a direct measure of the defect (accelerometer).