内燃机变分模态Rihaczek谱纹理特征识别诊断

Internal combustion engine fault diagnosis based on identification of variational modal Rihaczek spectrum texture characterization

针对内燃机故障诊断中振动响应信号强耦合、弱故障特征的问题,提出一种基于内燃机振动谱图纹理特征提取的故障诊断方法。首先,为了清晰地刻画内燃机振动信号时频联合分布中的非平稳时变分量,将变分模态分解(VMD)与Rihaczek复能量密度分布方法有效结合,得到了时频聚集性好、无交叉项干扰的内燃机振动谱图像;针对VMD分解过程中的参数选取问题,提出将功率谱熵作为目标函数,对VMD的分解参数进行网格寻优,提高了VMD分解的自适应性。为了实现对内燃机振动谱图像的自动识别及故障诊断,提出了改进的局部二值模式(ILBP)方法,用来对振动谱图中蕴含的纹理信息进行分析,提取低维特征参量并采用最近邻分类器对内燃机不同工况的振动谱图像进行模式识别。将该方法应用于内燃机故障诊断实例中,结果表明该方法能有效提取内燃机振动信号中的微弱故障特征,实现内燃机故障的自动诊断。

Internal combustion engine fault diagnosis using vibration response signal meets the challenging of strong coupling and weak fault characteristics. A fault diagnosis method is proposed based on texture feature extraction of internal combustion engine vibration spectrum. In order to clearly characterize the non-stationary time-varying components in the time-frequency distribution of internal combustion engine vibration signal,the variational mode decomposition（ VMD） is combined with the Rihaczek complex energy density distribution method. Thus,the vibration spectrum image can be obtained with good time-frequency clustering and no cross-term interference. Considering the parameter selection in the VMD decomposition process,Shannon entropy is introduced as the objective function and the successive grid search technique is employed to identify the optimal model parameters,to improve the adaptability of VMD decomposition. To realize the automatic recognition and fault diagnosis of the vibration spectrum of internal combustion engine,an improved local binary model（ ILBP） is presented to analyze the texture information contained in the vibration spectrum. The lowdimensional feature parameters are extracted and the nearest neighbor classifier is adopted to identify the vibration spectrum under different working conditions. The proposed method is applied to the fault diagnosis of internal combustion engine. The results show that the method can effectively extract the weak fault characteristics of the vibration signal and realize the automatic diagnosis of internal combustion engine failure.