Wyniki wyszukiwania - Biblioteka Nauki

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Multi-criterion fault diagnosis of rolling bearings

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Banach A. , Sułowicz M.

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PL

In this paper, the possibilities of rolling bearing diagnostics, according to the PN and ISO standards, utilising the dimensionless discriminants of vibroacoustic processes, CPB frequency analysis and envelope detection methods are presented. The test bench, the measuring system, as well as the obtained results are described in detail. The authors’ own algorithm for the course of action during the process of detecting damage torolling bearings, involving the multi-criterion diagnostic utilising the afore methods is also described.

2

Application of wavelet analysis for induction motor faults diagnostics operating at variable load

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Dulęba D. , Sułowicz M.

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PL

Technical condition assessment of induction motor can be done with stator current analysis. This analysis can be done in steady state with FFT transform. Starting current analysis with wavelet transform is a very good method of cage condition assessment. The advantage of this method is that we can observe how characteristic components vary in time. Characteristic fault symptoms can be observed when the machine is working with alternating load which causes rotor’s angular speed and slip change. Transient states when the load changes, occur very often increases the application of thism method. This paper presents the results of application wavelet transform in motor fault diagnostics in transient states.

3

The influence of mechanical faults on induction machines with electrical faults

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Gómez A. F. , Sobczyk T. J. , Sułowicz M.

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PL

The study of the interaction between faults of different natures is a crucial step for the development of effective systems for fault detection. The analysis of the electrical signals using the actual motor current signature analysis (MCSA) techniques may lead to different results and, as a consequence, an incorrect diagnosis of the fault due to the fact that the non-linear interactions between both faults are difficult to predict with high precision. The relationship between the electrical and mechanical components has been extensively studied in the past, but despite the progress made, the introduction of new control systems or the nonlinearities presented in the electrical machine, still makes it hard to diagnose faults. The study below attempts to show the evolution of the induction motor signatures when electrical and mechanical faults occur simultaneously. The Fourier analysis of the signatures presented in this paper indicates that the typical analysis carried out to diagnose the state of the electrical machine may be interpreted as an indicator of a different type of fault.

4

Application of interpolated discrete fourier transform method in the diagnostics of cage induction motors

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Krzyk P. , Pragłowska-Ryłko N. , Sułowicz M.

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PL

This paper compares the accuracy of the multi-frequency stator phase current spectrum estimation. This is as a development of considerations made in [1]. In this paper, the authors added to previous results for the classic Discrete Fourier Transform method and the two-point interpolated DFT method (IpDFT ), new results obtained by three-point IpDFT method’s algorithm. The analysis was made for slip spectrum component which is used for cage damage detection. To minimize the spectrum leakage effect, the selected Rife-Vincent Class I windows (RVCI ) were used. The article focuses on the effectiveness of spectral component estimation in function of the used method, type of time window and measurement time. Values of errors of frequency and amplitude estimations are presented on graphs. The authors compared theoretical considerations with the case of real stator current of an asynchronous motor with a broken cage. This article can be used as a base for further studies on the diagnosis of induction motors.

5

Dynamic and static eccentricity detection in induction motors in transient states

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Wroński W. , Sułowicz M. , Dziechciarz A.

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PL

The following paper presents possibilities for the application of selected time-frequency analysis methods in the fault detection of cage induction machines in transient states. The starting phase current of the machine was chosen as a diagnostic signal. Selected faults were eccentricities – static and dynamic. In order to increase the selectivity of the obtained signal transformations, a notch filter was used to remove the base harmonic of the phase current. Two approaches of fault detection were compared. In the first approach, the characteristic feature of fault was extracted using DWT analysis. Next, TMCSA methodology was applied in which characteristic harmonics related to faults were shown on a time-frequency plane. In this case, applied methods were a Gabor transformation, STFT, CWT and Wigner–Ville’s transformation. In the analysis, a phase current signal approximated by DWT was used. DWT approximation was applied to filter higher harmonics which improves the resolution of the obtained transformations.