66 publications from this institution
Use of a probabilistic model of vibration signals in the form of a periodically correlated random process allows new opportunities for diagnosing defects in rotating mechanisms at the early stages of their occurrence. Importance of multichannel simultaneous acquisition of vibration diagnostic signals for their join analysis grounded. General requirements to specialized portable devices for acquisition and preprocessing of the vibration diagnostic signals are considered.
The use of two different techniques for the analysis of vibration signals, whose carrier harmo¬nics are modulated by high-frequency narrow-band random processes is discussed. Periodically non-stationary random processes (PNRP) are suitable models for description of vibration signals of damaged mechanism. A proposed processing technique can be considered as an alternative to squared envelope analysis, kurtosis techniques, squared envelope spectrum (SES) and its use in the analysis of a vibration signal is discussed. It is shown that the spectral estimates obtained by the envelope square method are biased and inconsistent. The possibility of obtaining of the unbiased estimates by the PNVP method even for a signal/noise ratio equal to 0.07 has been demonstrated.
The result of the application of the spectral methods for searching and analysis of the hidden periodicities in the properties of the vibration signals of the rotary machinery which allow to discover the spectral band of the vibration periodical non-stationarity, to determine the spectrum forms, to evaluate the power of their time variation. The typical peculiarities of the defects are established on this basis, the new diagnostic indicators are formed for their detection and typification.
Hilbert transform of periodically non-stationary random signal (PNRS) is analyzed. The relations for covariance and spectral components of PNRS and its Hilbert transform are obtained. The analytic signal and envelope properties for multi-and monocomponent signal are analyzed.
Technical Diagnostics and Non-Destructive Testing, 2022, №04. TEKHNICHESKAYA DIAGNOSTIKA I NERAZRUSHAYUSHCHIY KONTROL Quarterly scientific-technical and production journal. ISSN: 0235-3474. Established in 1989. Subscription index: 74475. State Registration Certificate KV 4787 of 09.01.2001. All rights reserved Publisher: B.E.Paton Electric Welding Institute, National Academy of Sciences of Ukraine. Main topics of the magazine: theoretical and practical articles on technical diagnosing and non-destructive testing; flaw detection, residual life of structures. Each issue features: scientific articles; manufacturing experiences; reviews; exhibitions, conferences; advertisements.
The periodically non-stationary random signals (PNRSs), whose carrier harmonics are mo-dulated by jointly stationary high-frequency random processes are analyzed. A representation of the signal in the form of a superposition of high-frequency components is obtained and it is shown that these components are jointly periodically non-stationary random processes. The random process is periodically non-stationary of the second order only in the case when some of the cross-covariance functions of its modulation processes are not equal to zero. The correlations of the PNRP spectral harmonics and the correlations of the modulating processes in series representation are equivalent. Evaluating the specific features of the auto- and cross-covariances for modulating processes as well as contribution of each pair to the covariance component values allows us to detect defects at early stages.
It is shown that the models of gear pair vibration, proposed in literature, are particular cases of the bi-periodically correlated random processes (BPCRPs), which describe its stochastic recurrence with two periods. The possibility of vibration and analysis within the framework of BPCRP approximation, in the form of periodically correlated random processes (PCRPs), is grounded and the implementation of vibration processing procedures using PCRP techniques, which are worked out by the authors, is given. Searching for hidden periodicities of the first and the second orders was considered as the main issue of this approach. The estimation of the non-stationary period (basic frequency) allowed us to carry out a detailed analysis of the deterministic part, the covariance structure of the stochastic part, and to form, using their parameters, the sensitive indicators for fault detection. The results of the processing of the wind turbine gearbox vibration signals are presented. The amplitude spectra of the deterministic oscillations and the time changes of the stochastic part power for different fault stages are analyzed. The most efficient indicators, which are formed using the amplitude spectra for practical applications, are proposed. The presented approach was compared with known in literature cyclostationary analysis and envelope techniques, and its advantages are shown.
We discuss the use of the Hilbert transform for the analysis of periodically non-stationary random signals (PNRSs), whose carrier harmonics are modulated by jointly stationary high-frequency narrow-band random processes. PNRS of this type are suitable models for numerous natural and man-made phenomena, including the vibration of a damaged mechanism. We show that the auto-covariance function of the signal and its Hilbert transform are the same, and that their cross-covariance functions differ only in their sign, meaning that the sum of squares of the signal and its Hilbert transform cannot be considered a 'squared envelope' and no new information is contained compared with the variance of the raw signal. A representation of the signal in the form of a superposition of high-frequency components is obtained and it is shown that these components are jointly periodically non-stationary random processes. The properties of the band-pass filtered signals are examined, and it is shown that band-pass filtering can reduce both the number of signal variance cyclic harmonics and their amplitudes. We show that it is possible to extract the quadratures of narrow-band high-frequency modulation processes using the Hilbert transform. The results obtained here theoretically substantiate the use of the Hilbert transform for the analysis of high-frequency modulation which occurs when a fault appears. They offer a new way to consider the traditional approach to vibration diagnosis. A processing technique that can be considered an alternative to envelope analysis is described, and its use in the analysis of a vibration signal is discussed.
Hilbert transform of the wide-band high frequency amplitude modulation is considered. It is shown that its covariance function is the same as covariance function of the raw signal and cross-covariance functions of them differ only by a sign. It results in an identity of cyclic spectrums of variances for analytic and raw signals. The properties of band-pass filtered signals are examined and it is shown that band-pass filtering can reduce both the number of signal variance cyclic harmonics and their amplitudes. Relationships linking the covariance components of analytical signals and auto-covariance and cross-covariance functions of their quadratures are ascertained.
The strict characteristic and the comparative analysis of the developed by authors the coherent and the component methods for statistical analysis of periodically correlated random processes (PCRP) — the mathematical models of the stochastic oscillations — for unknown period of the non-stationarity is given. The comparison of efficiencies of the proposed methods for period estimation with the given in literature for individual cases of PCRP is provided.
