Local Damage Detection Method Based on Distribution Distances Applied to Time-Frequency Map of Vibration Signal

2018 ◽  
Vol 54 (5) ◽  
pp. 4091-4103 ◽  
Author(s):  
Grzegorz Zak ◽  
Agnieszka Wylomanska ◽  
Radoslaw Zimroz
Keyword(s):  
Damage Detection ◽  
Detection Method ◽  
Vibration Signal ◽  
Local Damage ◽  
Time Frequency ◽  
Frequency Map

scholarly journals Nonnegative Matrix Factorization of time frequency representation of vibration signal for local damage detection – comparison of algorithms

2018 ◽  
Vol 29 ◽  
pp. 00010
Author(s):  
Jacek Wodecki
Keyword(s):  
Damage Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Single Channel ◽  
Classical Method ◽  
Nonnegative Matrix ◽  
Vibration Signal ◽  
Local Damage ◽  
Time Frequency ◽  
Frequency Representation

Local damage detection in rotating machine elements is very important problem widely researched in the literature. One of the most common approaches is the vibration signal analysis. Since time domain processing is often insufficient, other representations are frequently favored. One of the most common one is time-frequency representation hence authors propose to separate internal processes occurring in the vibration signal by spectrogram matrix factorization. In order to achieve this, it is proposed to use the approach of Nonnegative Matrix Factorization (NMF). In this paper three NMF algorithms are tested using real and simulated data describing single-channel vibration signal acquired on damaged rolling bearing operating in drive pulley in belt conveyor driving station. Results are compared with filtration using Spectral Kurtosis, which is currently recognized as classical method for impulsive information extraction, to verify the validity of presented methodology.

Parametric Time-Frequency Map and its Processing for Local Damage Detection in Rotating Machinery

2013 ◽  
Vol 588 ◽  
pp. 214-222 ◽  
Author(s):  
Ryszard Makowski ◽  
Radoslaw Zimroz
Keyword(s):  
Damage Detection ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Time Varying ◽  
Local Damage ◽  
Time Frequency ◽  
Vibration Data ◽  
Novel Approach ◽  
Vibration Signal Analysis

The detection of local damage in rotating machinery (gears, bearings) via vibration signal analysis is one of the most powerful techniques in condition monitoring. However, in some cases, especially in heavy industrial machinery, it is difficult to detect damage because of the poor signal-to-noise ratio of the measured vibration. Therefore it is necessary to use unconventional advanced techniques to enhance the signal. In this paper, a novel approach based on parametric time-frequency analysis and further processing for: i) time-varying spectral content modelling, ii) the identification of informative frequency bands by statistical analysis, iii) local damage detection and iv) cycle identification via cepstral analysis, is presented. The proposed procedure is validated using real vibration data from bearings and gearboxes. It is worth noting that this methodology can be also successfully used in time-varying speed conditions (with limited fluctuation).

Non-dominated solution set based on time–frequency infograms for local damage detection of rotating machines

2019 ◽  
Vol 92 ◽  
pp. 213-227 ◽  
Author(s):  
Xingxing Jiang ◽  
Juanjuan Shi ◽  
Weiguo Huang ◽  
Zhongkui Zhu
Keyword(s):  
Damage Detection ◽  
Solution Set ◽  
Local Damage ◽  
Rotating Machines ◽  
Time Frequency

EXPERIMENTAL TESTS ON LOCAL DAMAGE DETECTION USING OPTICAL FBG SENSORS

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Jin-Hak Yi
Keyword(s):  
Damage Detection ◽  
Environmental Effects ◽  
Detection Method ◽  
Principal Component ◽  
Experimental Tests ◽  
Scale Model ◽  
External Loading ◽  
Local Damage ◽  
Offshore Structure ◽  
Load Variations

In this study, the fiber Bragg grating (FBG)-sensor based local damage detection method is proposed under circumstances with temperature and external loading variations. To compensate the environmental effects, principal component analysis (PCA) is utilized and also the performance of PCA is compared with that of the conventional linear adaptive filter (LF) model. Laboratory tests with a 1/20 scale model of a jacket-type offshore structure with six jacket-legs and a heavy super structure have been carried out for investigating the performance of the proposed damage detection method. From the experimental tests, it is observed that the local damage feature is mostly hidden and difficult to identify due to the environmental effects. By utilizing the conventional LF and PCA models, the effects of the undesirable environmental effects can be efficiently eliminated, and it is also found that the performances of the LF and PCA models are very similar and competitive to each other. However PCA model does not require the information on the temperature and external load variations, hence it can be concluded that the PCA-based local damage detection can be more efficiently applied for FBG-based local damage detection under temperature and external loading variations.

Magnetic Bearing Rotor Vibration Analysis Based on the Harmonic Wavelet Package Algorithm

2012 ◽  
Vol 588-589 ◽  
pp. 152-155
Author(s):  
De Guang Li ◽  
Shu Qin Liu
Keyword(s):  
Frequency Domain ◽  
Vibration Analysis ◽  
Vibration Signal ◽  
Magnetic Bearing ◽  
Rotor Vibration ◽  
Time Frequency ◽  
Optimizing Design ◽  
Harmonic Wavelet ◽  
Frequency Map

Analysis of the magnetic bearing rotor vibration is the base of the optimizing design, supervise and diagnosis of the magnetic bearing. Harmonic wavelet package was used for the analysis of the vibration signal, and the 3 dimension time-frequency domain energy map was constructed, then the analysis of the rotor vibration became convenient. Via analysis of the time-frequency map, the vibration in each time and each frequency was obtained, and the supervise and the diagnosis of the rotor can be realized.

Stochastic Modeling of Time Series with Application to Local Damage Detection in Rotating Machinery

2013 ◽  
Vol 569-570 ◽  
pp. 441-448 ◽  
Author(s):  
Jakub Obuchowski ◽  
Agnieszka Wylomanska ◽  
Radoslaw Zimroz
Keyword(s):  
Time Series ◽  
Good Condition ◽  
Real Data ◽  
Statistical Properties ◽  
Normal Operation ◽  
Local Damage ◽  
Data Set ◽  
Time Frequency ◽  
Frequency Map ◽  
Vibration Time

Raw vibration signals measured on the machine housing in industrial conditions are complex and can be modeled as an additive mixture of several processes (with different statistical properties) related to normal operation of machine, damage related to one (or more) of its part, some noise, etc. In the case of local damage in rotating machines, contribution of informative process related to damage is hidden in the raw signal so its detection is difficult. In this paper we propose to use the statistical modeling of vibration time series to identify these components. Building the model of raw signal may be ineffective. It is proposed to decompose signal into set of narrowband sub-signals using time-frequency representation. Next, it is proposed to model each sub-signal in the given frequency range and classify all signals using their statistical properties. We have used several parameters (called selectors because they will be used for selection of sub-signals from time-frequency map for further processing) for analysis of sub-signals. They have base in statistics theory and can be useful for example in testing of normality of data set (vibration time series from machine in good condition is close to Gaussian, damaged not). Results of such modeling will be used in the sub-signals classification procedure but also in defects detection. We illustrate effectiveness of novel technique using real data from heavy machinery system.

Damage Detection of a Gear with Initial Pitting Using the Zoomed Phase Map of Continuous Wavelet Transform

2006 ◽  
Vol 306-308 ◽  
pp. 223-228 ◽  
Author(s):  
Sang Kwon Lee ◽  
Jang Sun Shim ◽  
Byung-Og Cho
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Continuous Wavelet Transform ◽  
Vibration Signal ◽  
Phase Lag ◽  
Continuous Wavelet ◽  
Local Damage ◽  
Abrupt Increase ◽  
Phase Map

Impulsive vibration generated by localized gear damage can be used as an indicator for damage detection. Local damage induces an abrupt increase of the amplitude and phase lag of the impulsive vibration signal measured on the gearbox. Relatively large damage like “tip breakage” can be easily detected by the amplitude map of CWT (continuous wavelet transform) for the impulsive vibration signal measured on the gearbox. However, minor damage like “initial pitting” cannot be detected with the amplitude map. To overcome this problem, in this paper we take into account the phase map for a damage signal. The zoomed phase map of CWT is successfully applied to the detection of minor gearbox damage.

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