Novel Cyclo-Non-Stationary Indicators for Monitoring of Rotating Machinery Operating Under Speed and Load Varying Conditions

Condition monitoring arises as a valuable industrial process in order to assess the health of rotating machinery, providing early and accurate warning of potential failures and allowing for the planning and effective realization of preventative maintenance actions. In complex machines the failure indications of an early bearing damage are weak compared to other sources of excitations. Vibration analysis is most widely used and various methods have been proposed. In a number of applications, changes in the operating conditions (speed/load) influence the vibration sources and change the frequency and amplitude characteristics of the vibroacoustic signature, making them nonstationary. Recently an emerging interest has been focused on modelling rotating machinery signals as cyclostationary. Classical cyclostationary tools, such as Cyclic Spectral Correlation Density (CSCD) and Cyclic Modulation Spectrum (CMS), can be used to extract information about the cyclic behavior of cyclostationary signals, under the assumption of nearly constant rotating speed. Global diagnostic indicators have been proposed as a measure of cyclostationarity under steady operating conditions. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency as well as diagnostic indicators of cyclo-non-stationarity in order to cover the speed varying operating conditions. The scope of this paper is to propose a novel approach for the analysis of cyclononstationary signals to cover the simultaneous and independently varying speed and load operating conditions. The effectiveness of the approach is evaluated on simulated and real signals captured on a dedicated test rig.

GENERATION OF A PROBABILISTIC MODEL OF CYCLOSTATIONARY SIGNALS IN TRANSMISSION LINES OF ELECTRONIC DEVICES

При оценке целостности информационных сигналов, передаваемых по высокоскоростным линиям передачи, возникает необходимость в использовании вероятностных моделей случайных процессов. В существующих системах автоматизированного проектирования имитационное моделирование проводится для конечного числа реализаций сигнала, что ограничивает точность оценки статистическиххарактеристик. При проведении контрольных измерений применяются тестовые псевдослучайные последовательности конечной длительности. Контролируемые уровни вероятности ошибки могут составлять величины порядка 10-12. В таких случаях в программном обеспечении измерительной техники прибегают к оцениванию параметров вероятностных моделей. Существующие модели не позволяют проводить обработку сигналов для компенсации помех, вызванных электромагнитными наводками. В данной работе предложена модель двумерных плотностей вероятностей измеренных сигналов и помех, использование которой совместно с методом принципиальных и независимых компонент позволит в будущем компенсировать искажения полезного сигнала за счет наводок. When assessing the integrity of information signals transmitted on high-speed transmission lines, it is necessary to use probabilistic models of random processes. In existing computer-aided design systems, simulation is carried out for a finite number of signal realizations, which limits the prediction capabilities. During compliance tests of electronic devices test pseudo-random sequences of finite length are used. Controlled levels of probabilities can be less than 10-12. In such cases, the software functions provide an estimation of the parameters of probabilistic models for measurement equipment. The existing models do not allow to compensate for interference caused by electromagnetic crosstalk noise. In this paper, the model of two-dimensional probability densities of the measured signals and interference is introduced. This model combined with well-known methods of principal and independent components will allow compensating for the distortion of the useful signal due to interference.

A Recursive Frequency Smoothing Method of Cyclic Spectrum Density Estimation

Cyclic spectrum density estimation plays a significant role in cyclostationary signals analysis. Generally, the methods of cyclic spectrum density estimation are mainly focus in time-domain, such as FFT Accumulation Method (FAM) and Strip Spectral Correlation Algorithm (SSCA). In this paper, based on the principle of frequency smoothing methods, an improved method in frequency-domain was proposed, which could reduce the computational cost greatly by using recursion. The recursive method was based on the fact that the range difference of smoothing window was small when window slid. In other words, we could get a new point of cyclic spectrum density just by computing the complex conjugate products of newly coming into window. In addition, the simulation results were given at last, which showed the good performance of our proposed method.