Jindrich Liska

The Use of Time-Frequency Methods in Rotor/Stator Impact-Rubbing Detection

At present, detailed rubbing diagnostics is implemented as off-line analysis of measured data. During the machine operation, the overall level of vibration and the rotational frequency (1X) phasor are traced. On the basis of amplitude and phase changes, the presence of rub is identified. Although unmonitored, changes in sub synchronous spectral components are also caused by rubbing. The new method presented herein utilizes measurements of these changes for better online rubbing detection. The paper presents the authors work on rotor/stator rub detection in steam turbines. For automatic rubbing detection, an on-line tool, called cumulative full-spectra method and based on full-spectra computation, is proposed. The proposed automatic detection algorithm is based on characteristic value, which is computed using special norming of tracked frequencies in the cumulative full-spectra.© 2011 ASME

Diagnostic methods of a bladed disc mode shape evaluation used for shrouded blades in steam turbines

This paper deals with advanced methods for the evaluation of a bladed disc behavior in terms of the wheel vibration and blade service time consumption. These methods are developed as parts of the noncontact vibration monitoring system of the steam turbine shrouded blades. The proposed methods utilize the time-frequency processing (cross spectra) and the method using least squares to analyse the data from the optical and magnetoresistive sensors, which are mounted in the stator radially above the rotor blades. Fundamentally, the blade vibrations are detected during the blade passages under the sensors and the following signal processing, which covers also the proposed methods, leads to the estimation of the blade residual service life. The prototype system implementing above mentioned techniques was installed into the last stage of the new steam turbine (LP part). The methods for bladed disc mode shape evaluation were successfully verified on the signals, which were obtained during the commission operation of the turbine.

Time-frequency methods for signal analysis in wind turbines

Since wind turbines became one of the most often source of renewable energy, appropriate health and condition monitoring systems are required. Especially proper monitoring of offshore plants is very significant because the accessibility is difficult and inspections are very costly. In comparison with conventional rotating machine vibration monitoring, where steady conditions and stationary signal are usually assumed, the wind turbines are characterized by unsteady conditions due to variable rotational speed. Hence the vibration signal is non-stationary and interpretation of signal signatures may be more complex. The common approach to analyze such non-stationary signals is the use of a time-frequency method, usually Short-Time Fourier Transform, which is the most popular one due to its simplicity. Nevertheless, there are other methods which can give a different view at the analyzed data and provide new information. This article investigates the potential use of some other time-frequency methods, namely Wavelet Transform, Wigner-Ville distribution and Hilbert-Huang transform in wind plants monitoring systems and apply these methods to real measured data with additional simulated bearing fault signal. Finally, the mentioned methods are compared based on computational complexity, readability and interpretability. Though the last two criteria are very subjective, Short-Time Fourier Transform was finally chosen as the most effective method followed by Wavelet Transform.