滚动轴承压缩故障信号的特征代理与凸优化重构算法

Feature proxy and convex optimization reconstruction algorithm for rolling bearing compressed fault signal

压缩采样可以有效缓解机械状态监测数据存储和传输的压力,但是压缩数据的感知重构一直是个难点。针对滚动轴承压缩信号的故障特征提取问题,提出一种基于特征代理与凸优化算法的故障信号重构方法。分析了滚动轴承局部故障信号的稀疏和卷积特性,学习得到故障冲击模式。对压缩得到的轴承观测信号,构造包含冲击时刻特征的代理,并对代理建立目标优化函数,采用快速迭代收缩阈值算法(Fast Iterative Shrinkage Threshold Algo⁃rithm,FISTA)直接从代理提取出稀疏系数,将学习模式与稀疏系数卷积重构出故障信号。与直接利用FISTA从压缩信号中提取冲击特征相比,所提方法在不降低求解精度的同时降低了计算复杂度。相比于常用的贪婪类重构算法,所提方法无需预先估计信号的稀疏度,且能得到全局最优解。通过滚动轴承仿真和实验信号进一步验证了所提方法的有效性。

Although compressed sampling can relieve the pressure of data storage and transmission in the field of mechanical condi-tion monitoring,the reconstruction of compressed data has always been a challenge.Aiming at the problem of fault extraction from compressed rolling bearing signals,a reconstruction method for fault signal based on the feature proxy and convex optimization al-gorithm is developed.The sparse and convolutional characteristics of localized bearing fault signal are analyzed,and the fault im-pact pattern is learned.For the compressed bearing signal,the proxy containing the information of impact occurrence time is con-structed.An optimization function is established for the proxy,and the sparse coefficient is extracted from the proxy with the Fast Iterative Shrinkage Thresholding Algorithm(FISTA).Finally,the fault signal is reconstructed by the convolution between the im-pact pattern and the sparse coefficient.Compared with the direct impact feature extraction from compressed signal using the FIT-SA,the proposed method reduces the computational complexity while without reducing the accuracy of the solution.When com-pared with the commonly used greedy reconstruction algorithm,the proposed method does not require the prior estimation of signal sparsity,and can get the global optimal solution.The rolling bearing simulation and experimental analysis further verify the effec-tiveness of the proposed method.