Unified time–scale–frequency analysis for machine defect signature extraction: Theoretical framework

Wang, Changting; Gao, Robert X.; Yan, Ruqiang

doi:10.1016/j.ymssp.2008.03.017

Cited by 22 publications

(5 citation statements)

References 24 publications

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“…To intuitively illustrate the superiority of the proposed method, a series of simulated signals is utilized to evaluate the effects of this method. In the following, the proposed method is verified in comparison with three traditional methods, which are the empirical mode decomposition-based (EMD-based) spectral analysis method [35,36], the wavelet transform (via DWT) with post spectral analysis method [37] (the result of which is called wavelet spectrum), and MSTSR based on DWT. The test signals are generated according to the equation below: ).…”

Section: Simulation Studymentioning

confidence: 99%

A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains

2016

Meas. Sci. Technol.

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It is very difficult to detect weak fault signatures due to the large amount of noise in a wind turbine system. Multiscale noise tuning stochastic resonance (MSTSR) has proved to be an effective way to extract weak signals buried in strong noise. However, the MSTSR method originally based on discrete wavelet transform (DWT) has disadvantages such as shift variance and the aliasing effects in engineering application. In this paper, the dual-tree complex wavelet transform (DTCWT) is introduced into the MSTSR method, which makes it possible to further improve the system output signal-to-noise ratio and the accuracy of fault diagnosis by the merits of DTCWT (nearly shift invariant and reduced aliasing effects). Moreover, this method utilizes the relationship between the two dual-tree wavelet basis functions, instead of matching the single wavelet basis function to the signal being analyzed, which may speed up the signal processing and be employed in on-line engineering monitoring. The proposed method is applied to the analysis of bearing outer ring and shaft coupling vibration signals carrying fault information. The results confirm that the method performs better in extracting the fault features than the original DWT-based MSTSR, the wavelet transform with post spectral analysis, and EMD-based spectral analysis methods.

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Section: Simulation Studymentioning

confidence: 99%

A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains

2016

Meas. Sci. Technol.

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“…Based on the current state of the art, it should be concluded that both methods can complement each other; therefore, a proper combination of both methods can be used successfully for proper diagnostics of signals. A proper combination of both algorithms has been used to identify defects of bearings in [ 18 , 19 , 20 ]. Nonetheless, the use of a hybrid method based on the time-scale-frequency analysis to diagnose measurement signals provides potentially vast possibilities, and it requires a broader analysis.…”

Section: Introductionmentioning

confidence: 99%

Possibilities of a Hybrid Method for a Time-Scale-Frequency Analysis in the Aspect of Identifying Surface Topography Irregularities

et al. 2023

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The article presents research results related to assessing the possibilities of applying modern filtration methods to diagnosing measurement signals. The Fourier transformation does not always provide full information about the signal. It is, therefore, appropriate to complement the methodology with a modern multiscale method: the wavelet transformation. A hybrid combination of two algorithms results in revealing additional signal components, which are invisible in the spectrum in the case of using only the harmonic analysis. The tests performed using both simulated signals and the measured roundness profiles of rollers in rolling bearings proved the advantages of using a complex approach. A combination of the Fourier and wavelet transformations resulted in the possibility to identify the components of the signal, which directly translates into better diagnostics. The tests fill a research gap in terms of complex diagnostics and assessment of profiles, which is very important from the standpoint of the precision industry.

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“…One of the important points which needs to be affirmed in wavelet packet-based techniques is the fact that frequency order is not the same as the node order due to down sampling [15]. To tackle this issue, the fine resolution advantage of wavelet packet decomposition (WPD) integrated with the spectral analysis of Fourier transform to extract the most appropriate sub-band including rotor fault feature frequency [17,18].…”

Section: Introductionmentioning

confidence: 99%

Broken Rotor Bar Fault Detection and Classification Using Wavelet Packet Signature Analysis Based on Fourier Transform and Multi-Layer Perceptron Neural Network

et al. 2017

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As a result of increasing machines capabilities in modern manufacturing, machines run continuously for hours. Therefore, early fault detection is required to reduce the maintenance expenses and obviate high cost and unscheduled downtimes. Fault diagnosis systems that provide features extraction and patterns classification of the fault are able to detect and classify the failures in machines. The majority of the related works that reported a procedure for detection of rotor bar breakage so far have applied motor current signal analysis using discrete wavelet transform. In this paper, the most appropriate features are extracted from the coefficients of a wavelet packet transform after fast Fourier transform of current signal. The aim of this study is to develop an effective and sensitive method for fault detection under low load conditions. Through combining the strength of both timescale and frequency domain analysis techniques, a unified wavelet packet signature analysis pinpoints the fault signature in the special fault-oriented frequency bands. The wavelet analysis combined with a feed-forward neural network classifier provides an intelligent methodology for the automatic diagnosis of the fault severity during runtime of the motor. The faults severity is considered as one, two, and three broken rotor bars. The results have confirmed that the proposed method is effective for diagnosing rotor bar breakage fault in an induction motor and classification of fault severity.

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Unified time–scale–frequency analysis for machine defect signature extraction: Theoretical framework

Cited by 22 publications

References 24 publications

A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains

A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains

Possibilities of a Hybrid Method for a Time-Scale-Frequency Analysis in the Aspect of Identifying Surface Topography Irregularities

Broken Rotor Bar Fault Detection and Classification Using Wavelet Packet Signature Analysis Based on Fourier Transform and Multi-Layer Perceptron Neural Network

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