Abstract
Wind turbine gearboxes operate in harsh environments; therefore, the resulting gear vibration signal has characteristics of strong nonlinearity, is non-stationary, and has a low signal-to-noise ratio, which indicates that it is difficult to identify wind turbine gearbox faults effectively by the traditional methods. To solve this problem, this paper proposes a new fault diagnosis method for wind turbine gearboxes based on generalized composite multiscale Lempel–Ziv complexity (GCMLZC). Within the proposed method, an effective technique named multiscale morphological-hat convolution operator (MHCO) is firstly presented to remove the noise interference information of the original gear vibration signal. Then, the GCMLZC of the filtered signal was calculated to extract gear fault features. Finally, the extracted fault features were input into softmax classifier for automatically identifying different health conditions of wind turbine gearboxes. The effectiveness of the proposed method was validated by the experimental and engineering data analysis. The results of the analysis indicate that the proposed method can identify accurately different gear health conditions. Moreover, the identification accuracy of the proposed method is higher than that of traditional multiscale Lempel–Ziv complexity (MLZC) and several representative multiscale entropies (e.g., multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).
Highlights
Wind turbines are widely used in the power system field, and are mainly composed of an impeller, rotor, gearbox, generator, bearing and coupling
To effectively identify wind turbine gearbox faults, this paper proposes a new intelligent fault diagnosis scheme based on morphological-hat convolution operator (MHCO) and generalized composite multiscale Lempel–Ziv complexity (GCMLZC), which is mainly composed of four stages
In order to simultaneously obtain fault feature information of the vibration signal at different levels and scales, the idea of hierarchical decomposition can be integrated into the GCMLZC to further propose generalized hierarchical multiscale Lempel–Ziv complexity (GHMLZC), which is regarded as a future research direction
Summary
Wind turbines are widely used in the power system field, and are mainly composed of an impeller, rotor, gearbox, generator, bearing and coupling. To avoid the calculation deviation of MLZC brought by data length shortening and improve the integrality and veracity of fault feature extraction, by integrating generalized composite coarse-grained process into LZC, this paper proposes a new complexity index named generalized composite multiscale Lempel–Ziv complexity (GCMLZC) to extract more accurately and efficiently fault feature information and identify fault categories. The noise reduction ability of existing single-scale MF methods (e.g., the dilation, erosion, opening and closing operator) is finite, and the scale selection of the structuring element (SE) of MF highly depends on human experience [24] To address this issue, by combining the merits of multiscale morphological analysis and convolution operation in noise reduction, this paper presents a morphological convolution filtering technique named multiscale morphological-hat convolution operator (MHCO) to preprocess the collected original vibration signal, where the SE scale is determined automatically by introducing the assisted index named the signal characteristic frequency-to-noise ratio (SCFNR). Where three operators (i.e., GDE, GCO and GCOOC) belong to the morphological gradient operator, whereas another three operators (i.e., AHDE, AHCO and AHCOOC) belong to the morphological average-hat operator
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