Abstract
The defect characteristics of rolling bearing are difficult to excavate at the incipient injury phase; in order to effectively solve this issue, an original strategy fusing recursive singular spectrum decomposition (RSSD) with optimized cyclostationary blind deconvolution (OCYCBD) is put forward to achieve fault characteristic enhanced detection. In this diagnosis strategy, the data-driven RSSD method without predetermined component number is proposed. In addition, a new morphological difference operation entropy (MDOE) indicator, which takes advantage of morphological transformation and Shannon entropy, is developed for confirming the influencing parameters of cyclostationary blind deconvolution (CYCBD). During the process of fault detection, RSSD is firstly adopted to preprocess the original signal, and the most sensitive singular spectrum component (SSC) is selected by the envelope spectrum peak (ESP) indicator. Then, the grid search algorithm is adopted to precisely confirm the optimal parameters and OCYCBD is further performed as a postprocessing technology on the most sensitive component to suppress the residual interferences and amplify the fault signatures. Finally, the enhanced fault detection of rolling bearing is able to achieve by analyzing the envelope spectrum of deconvolution signal. The feasibility of the proposed strategy is verified by the simulated and the measured signals, respectively, and its superiority is also demonstrated through several comparison methods. The results manifest this novel strategy has praisable advantages on weak characteristic extraction and intensification.
Highlights
As the joint of wind turbine, rolling bearing is indispensable and important component during wind turbine operation
Some inherent weaknesses of these methods impair their performances on fault characteristic extraction. e classic wavelet decomposition does not possess adaptive processing ability because mother wavelet function and decomposition level need to be predefined in advance [7]. e strict mathematical derivations are lacking in local mean decomposition (LMD) and empirical mode decomposition (EMD), which suffer from the noise sensibility, the endpoint divergence, and the aliasing effect problems, and these problems may cause the obtained signal components losing specific meanings [8]
An original enhanced fault detection strategy called RSSDOCYCBD is presented to identify the local defect of rolling bearing in the early injury stage
Summary
As the joint of wind turbine, rolling bearing is indispensable and important component during wind turbine operation. In the view of above statements, the proposed RSSD and OCYCBD methods can be naturally combined to exert their complementary advantages for dealing with the weak injury identification problem of rolling bearing, and an enhanced fault detection strategy based on RSSD-OCYCBD is put forward in this paper. Within this strategy, the proposed RSSD method, which can overcome the drawback of component number artificial selection in the original SSD algorithm, is firstly utilized as a preprocessing approach to separate the most sensitive SSC automatically.
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