【摘要】：針對內燃機故障診斷中振動響應信號強耦合、弱故障特征的問題,提出一種基于內燃機振動譜圖紋理特征提取的故障診斷方法。首先,為了清晰地刻畫內燃機振動信號時頻聯(lián)合分布中的非平穩(wěn)時變分量,將變分模態(tài)分解(VMD)與Rihaczek復能量密度分布方法有效結合,得到了時頻聚集性好、無交叉項干擾的內燃機振動譜圖像;針對VMD分解過程中的參數選取問題,提出將功率譜熵作為目標函數,對VMD的分解參數進行網格尋優(yōu),提高了VMD分解的自適應性。為了實現(xiàn)對內燃機振動譜圖像的自動識別及故障診斷,提出了改進的局部二值模式(ILBP)方法,用來對振動譜圖中蘊含的紋理信息進行分析,提取低維特征參量并采用最近鄰分類器對內燃機不同工況的振動譜圖像進行模式識別。將該方法應用于內燃機故障診斷實例中,結果表明該方法能有效提取內燃機振動信號中的微弱故障特征,實現(xiàn)內燃機故障的自動診斷。
[Abstract]:Aiming at the problem of strong coupling and weak fault characteristics of vibration response signals in internal combustion engine fault diagnosis, a fault diagnosis method based on texture feature extraction of internal combustion engine vibration spectrum is proposed. Firstly, in order to describe clearly the nonstationary time-varying components in the time-frequency joint distribution of internal combustion engine vibration signals, the variational mode decomposition (VMD) method and the Rihaczek complex energy density distribution method are effectively combined to obtain good time-frequency aggregation. In view of the problem of parameter selection in the process of VMD decomposition, the power spectrum entropy is used as the objective function to optimize the decomposition parameters of VMD in order to improve the self-adaptability of VMD decomposition. In order to realize the automatic identification and fault diagnosis of internal combustion engine vibration spectrum image, an improved local binary mode (ILBP) method is proposed to analyze the texture information contained in the vibration spectrum image. The low dimensional characteristic parameters are extracted and the nearest neighbor classifier is used to recognize the vibration spectrum images of internal combustion engines under different working conditions. The method is applied to the fault diagnosis of internal combustion engine. The results show that the method can effectively extract the weak fault characteristics from the vibration signal of internal combustion engine and realize the automatic fault diagnosis of internal combustion engine.
【作者單位】： 火箭軍工程大學理學院;
【基金】：國家自然科學基金(51405498) 中國博士后基金(2015M582642)項目資助
【分類號】：TK407;TP391.41

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