Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

Abstract

The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extract and enhance the fault feature effectively. In this diagnosis method, the fast spectral coherence is fused with the equal step size search strategy for the cyclic frequency parameter and the filter length parameter optimization, and a new frequency weighted energy entropy (FWEE) indicator which combining the advantages of the frequency weighted energy operator (FWEO) and the Shannon entropy, is developed for deconvolution signal evaluation during parameter optimization process. In addition, a novel singular value order determination approach based on fitting error minimum principle is utilized by SVDD to enhance the fault feature. During the process of defect identification, OCYCBD with the optimal parameters is firstly used to recover the informative source from the collected vibration signal. FWEO is further utilized to highlight the potential impulsive characteristics, and the instantaneous energy signal of deconvolution result can be acquired. The whole interferences contained in the instantaneous energy signal can’t be removed due to the weak fault signature and the severe background noise. Then, SVDD is applied to purify the instantaneous energy signal of deconvolution signal, by which the residual interference component is eliminated and the fault feature is strengthened immensely. Finally, frequency domain analysis is performed on the denoised instantaneous energy signal, and the defect location identification of wind turbine bearing can be achieved through analyzing the obvious spectral lines in the obtained enhanced energy spectrum. The collected signals from the experimental platform and the engineering field are both utilized to verify the feasibility of proposed method, and its superiority is further demonstrated through comparing with several well known diagnosis methods. The results indicate this novel method has distinct advantage on bearing weak feature extraction and enhancement.

Application of RSSD-OCYCBD Strategy in Enhanced Fault Detection of Rolling Bearing

“…As for filter length selection, there is no reference. Literature [27] has shown the performance of CYCBD is not proportional to the filter length. Whether the selected filter length L is too big or too small, it will not be conducive to fault characteristic extraction.…”

A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis

“…To explain Formula (1), let us give an example, assuming that x = (4,8,9,6,5,11,7). When τ = 1, m = 3, five embedding vectors can be obtained as:…”

“…As a key component of rotating machinery, rolling bearings play an important role in rotating machinery [1][2][3][4][5]. Due to the complicated structure and harsh operating environment, various faults of rolling bearings (inner ring fault, outer ring fault, ball fault) is inevitable; thus, it is of great significance to study the fault detection methods and diagnostic techniques of rolling bearings [6][7][8][9][10]. In order to find out the fault location, the current frequently used fault diagnosis method is the time-frequency analysis method, such as the variable mode decomposition, local mean mode decomposition, empirical mode decomposition and so on [11][12][13][14].…”

Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

“…Basing on the above literature discussion, it can be fairly stated that many powerful techniques for the analysis of wind turbine vibration signatures are based on cyclo-stationarity. The downside is that this kind of analysis is particularly demanding as regards the data because, for example, the angular speed must be measured at high sampling rates, and this is not guaranteed even by using time-resolved operation data (as, for example, the ones analyzed in [22,23]): for this reason, most of the studies deal with numerical simulations [19] and laboratory test rig measurements [24]. On the other side, it should be noticed that industrial wind turbines are commonly equipped with commercial condition monitoring systems: these do not stock measurements continuously (they record when some trigger events occur) and stock them in treated form instead of raw.…”