基于改进CYCBD的滚动轴承复合故障自适应诊断方法

为实现滚动轴承复合故障自适应诊断，该研究提出了基于循环含量比-归一化谐波比例（Ratio of Cyclic Content-Normalized Proportion of Harmonics，RCC-NPH）融合指标改进的最大二阶循环平稳盲解卷积（Maximum second order cyclostationary blind deconvolution，CYCBD）方法。首先，构建了RCC-NPH融合指标，解决了CYCBD算法循环频率确定依赖先验知识及遍历所有故障频率空间耗时的问题。其次，根据RCC-NPH融合指标图估计CYCBD的循环频率集，实现了CYCBD参数的自适应选择。再次，采用自适应参数CYCBD方法对输入信号进行解卷积运算，提取了不同循环频率对应的故障信号。最后，对提取的故障信号进行Hilbert包络解调分析，完成故障的辨识。利用该方法分别对仿真信号和轴承复合故障信号进行试验，均能有效检测信号中包含的故障成分，实现了复合故障的自适应诊断。与其他指标相比，该方法能够有效避免噪声和谐波的干扰，适用于复合故障诊断。

Adaptive diagnosis method of composite fault for rolling bearings using improved CYCBD

Abstract: Rolling bearing is the core component of large rotating machinery in agricultural engineering. The composite fault is more harmful than the single fault in the process of operation. The source signals of composite faults are coupled with each other through the convolution in the process of propagation, which brings difficulties to fault detection. The maximum second-order cyclostationary blind deconvolution (CYCBD) can be used to reduce the influence of the transmission path using the deconvolution process. The mutual coupling between signals can be eliminated to effectively extract the periodic pulse signals. However, the CYCBD cycle frequency is directly related to the cycle stability of deconvolution signals. There is a great influence on deconvolution. The fault characteristic frequency depends mainly on the manual experience or optimization. It is a high demand to determine the composite fault diagnosis of rolling bearings in production practice. This study aims to extract the composite fault features of rolling bearings for the adaptive diagnosis of composite faults. An improved composite fault diagnosis was proposed for the CYCBD rolling bearings using RCC-NPH fusion index. Firstly, an investigation was made to comprehensively characterize the composite fault signals, then to integrate the ratio of cyclic content (RCC) and normalized proportion of harmonics (NPH) indexes. A new RCC-NPH fusion index was also proposed to consider the signal SNR, impact property, and harmonic components. As such, the CYCBD was independent of the prior knowledge to determine the cycle frequency covering all the fault frequency space. Secondly, the cycle frequency of CYCBD was set adaptively, according to the RCC-NPH fusion index. The cycle frequency dataset was also set to achieve the adaptive selection of CYCBD parameters. Thirdly, the parameter adaptive CYCBD served as the deconvolution on the input composite fault signals. The fault signals corresponding to different fault frequencies were then extracted to realize the effective separation of composite faults. Finally, the extracted single fault signal was demodulated by the Hilbert envelope to realize the fault identification. An experimental platform was developed to verify the improved model using the simulation signals and the experimental data. Experimental results show that the improved model with the RCC-NPH fusion index accurately and efficiently estimated the cycle frequency in the line with the characteristics of the signal. The CYCBD was also independent of the prior knowledge on the composite fault diagnosis. At the same time, the RCC-NPH fusion index effectively suppressed the interference frequency, in order to visually depict the composite fault features. An accurate extraction was realized for the fault components contained in the signals by systematic comparison with four indexes, including the RCC, NPH, autocorrelation spectrum, and multi-point kurtosis spectrum. The mutual coupling between signals was eliminated to successfully extract each single fault component after adaptive fault diagnosis for the rolling bearing composite faults. The comprehensive diagnosis of composite faults was realized to effectively avoid the misdiagnosis and missed diagnosis. Therefore, the composite fault adaptive diagnosis can be expected to effectively identify and separate each single fault feature in the composite fault, particularly for the adaptive diagnosis of rolling bearing composite faults.