Localized fault identification of planetary gearboxes based on multiple-domain manifold
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摘要: 行星齒輪箱振動信號包含多種頻率成分和噪聲干擾，頻譜具有復雜的邊帶結構，容易對故障識別造成誤導甚至引起錯判.在不同故障狀態下，行星齒輪箱振動信號的多域特征量將偏離正常范圍且偏離程度不同，根據這一特點，提取振動信號的時域、頻域特征參量用于故障識別.為了避免傳統分析方法中負頻率及虛假模態問題，增強對噪聲干擾的魯棒性，采用局部均值分解法將信號自適應地分解為單分量之和，提取時頻域單分量瞬時幅值能量.針對多域特征空間構造過程中出現的高維及非線性問題，采用流形學習對數據進行降維處理.提出基于改進的虛假近鄰點的本征維數估計及最優k鄰域確定方法，并通過等距映射對多域特征空間進行降維分析.對于行星齒輪箱實驗信號，根據樣本流形特征聚類結果，分別識別出了太陽輪、行星輪和齒圈的局部故障，從而驗證了上述方法的有效性.Abstract: The vibration signals of planetary gearboxes are composed of complex frequency components and interfering noises, and their spectra have intricate sidebands, which cause difficulty in and even misleading fault identification. In different fault cases, the vibration signatures in multiple domains typically differ from normal states with different discrepancies. Based on this hypothesis, time and frequency domain features are extracted for the purposes of fault identification. The vibration signal is adaptively decomposed into a set of mono-components, and the instantaneous energy of each mono-component is calculated in time-frequency domain by exploiting the merits of local mean decomposition, including its better robustness to noise and freedom from pseudo-mode and negative frequency problems. Manifold learning is utilized to tackle the high-dimensionality and non-linearity aspects of multiple-domain feature space construction. A new method is proposed for estimating the intrinsic dimension and selecting the k-nearest neighborhood based on the improved pseudo-nearest neighbor. In addition, isometric feature mapping (ISOMAP) is utilized to reduce the dimensions of the multiple-domain feature space. The proposed method is validated by analyzing the planetary gearbox lab experimental dataset. Based on the clustering analysis results of the extracted manifold features, the localized faults on the sun, planet, and ring gears are successfully identified.
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參考文獻
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