Len Gelman

Induction motor bearings diagnostic using MCSA and normalized tripple covariance

Diagnosis of induction motors, conducted remotely by measuring and analyzing the supply current is attractive with the lack of access to the engine. So far there is no solution, based on analysis of current, the credibility of which allow use in industry. Statistics of IM bearing failures of induction motors indicate, that they constitute more than 40% of IM damage, therefore bearing diagnosis is so important. The article provides an overview of selected methods of diagnosis of induction motor bearings, based on measurement of the supply current. The problem here is the high disturbance components level of the motor current in relation to diagnostic components. The paper presents the new approach to signal analysis solutions, based on statistical methods, which have been adapted to be used by this diagnostic system. First experimental results with use of this method are also presented, they confirm the advantages of this method.

A new time-frequency transform for non-stationary signals with any nonlinear instantaneous phase

A new generic adaptive time-frequency transform based on the Wigner distribution is proposed for amplitude estimation of transient signals with any nonlinear non-polynomial variation of the instantaneous frequency. It is for use in situations where independent synchronous measurements of the instantaneous frequency are available. It shows that the new transform tracks the instantaneous frequency and estimates signal amplitude without errors along the curve of the instantaneous frequency. The paper also applies the new transform to signals with the non-linear sinusoidal and exponential variations in the instantaneous phase and determines formulae for transform in these cases. The paper compares the new transform with the Wigner distribution in several cases and demonstrates that the new transform is more effective at amplitude estimation of signals with nonlinear variation of the instantaneous frequency. New analytic formulae are obtained for the new transform and the Wigner distribution in both the sinusoidal and the exponential cases. An analytic formula is obtained which relates the new transform to the Wigner distribution in the sinusoidal case. This formula is inverted to obtain a previously unknown formula for the Wigner distribution of any signal. It is shown that the new transform could be used for adaptive processing of transient signals with instantaneous frequency variations. The paper studies the performance of the new transform with no adaptation, partial adaptation and complete adaptation.

Condition Monitoring of Fatigue Cracks in Machinery Blades

A novel generic approach to fatigue crack diagnostics in machinery blades is proposed and employed. The approach consists of simultaneously using two new diagnostic features: the real and imaginary parts of the Fourier transform of vibroacoustical signal generated from a blade. This approach is more generic than traditional approach based on the power spectral density; the power spectral density is a particular case of the proposed approach. Numerical examples are given based on the processing of signals generated using a nonlinear model of a blade. The signals generated are the resonant vibroacoustical oscillations of cracked and uncracked blades under narrowband vibration excitation. The numerical examples show that the crack detection is more effective when using the new approach than when using the power spectral density approach. The presented experimental results are matched with the numerical results. The proposed approach offers an effectiveness improvement over the traditional approach based on power spectral density.