基于多因子进化稀疏重构的轴承故障诊断研究

Research on bearing fault diagnosis based on multi-factor evolutionary sparse reconstruction

针对强噪声背景下滚动轴承振动信号特征提取困难的问题,基于稀疏表示基本理论,提出一种使用多因子进化算法求解的多正则化稀疏重构降噪模型。首先,将多正则化模型的求解划分为多3个目标子任务,即l0范数约束优化主任务和l1、l1/2范数正则化额外任务,以上任务分别构成3个不同目标的多因子优化稀疏重构算法;其次,根据在进化过程中不同正则化任务的优先级,采用黄金分割搜索策略保证每个族群包含相似适应度的个体,通过两点交叉遗传算子保证样本的稀疏性特征;最后,将阈值迭代算法应用于局部搜索过程加速子任务中的种群收敛。在此理论基础之上,分别通过仿真信号和实际轴承数据验证本文方法可行性,发现在-10 dB的高斯噪声干扰下,重构信号的信噪比依然达到5 dB。试验结果表明,该方法可有效提取强噪声背景下的冲击特征,为进一步的故障诊断提供可靠先验知识。

Aiming at the problem of difficult feature extraction of rolling bearing vibration signals in the strong noise background,based on the basic theory of sparse representation,a multi-regularized sparse reconstruction noise reduction model using multi-factor evolutionary algorithm is proposed.Firstly,the solution of the multi-regularization model is divided into three more objective subtasks,the l0-paradigm constrained optimization main task and the l1 and l1/2-paradigm regularization additional tasks,and the above tasks constitute three different objectives of the sparse reconstruction algorithm for multi-factor optimization;secondly,according to the priority of different regularization tasks in the evolutionary process,the golden segmentation search strategy is used to ensure that each community contains individuals with similar fitness,and the sparsity characteristics of the samples are guaranteed by the two-point crossover genetic operator;lastly,the thresholding iterative algorithm is applied to the local search process to accelerate the population convergence in the subtask.On this theoretical basis,the feasibility of this method is verified by simulation signal and actual bearing data respectively,and it is found that the signal to Interference plus noise ratio(SNR)of the reconstructed signal still reaches 5 dB under the interference of Gaussian noise of-10 dB.The experimental results show that this method can effectively extract the impact features under the background of strong noise,and provide reliable a priori knowledge for further fault diagnosis.