并联基追踪稀疏分解在齿轮箱弱故障诊断中的应用

Application of sparse signal decomposition using dual-BP in gear-box weak fault diagnosis

为了提取齿轮箱振动信号淹没在强背景噪声中的早期微弱冲击故障信息,先利用不同长度的窗函数的短时傅里叶变换对信号进行稀疏分解,得出初始分解系数,再利用并联基追踪对处理得到的系数进行优化处理,最后对得到的系数进行重构,分别得到信号的持续振荡成分及故障冲击成分,进一步对故障冲击成分分析得出诊断结果。仿真信号分析及应用实例分析结果表明了算法的可行性及有效性,为强噪声环境下的机械故障信号提取提供了一种新的思路。

In order to extract the early weak impact fault information in gearbox vibration signal submerged in strong background noise, a new sparse signal decomposition method using dual-BP is proposed. Firstly, the short-time Fourier transform using the window functions with different lengths is adopted to perform the signal sparse decomposition, and the initial decomposition coefficients are obtained; secondly, the dual-BP is used to optimize the obtained decomposition coefficients; lastly, the obtained coefficients are reconstructed, and the sustained oscillation component and the failure impact component of the signal are obtained; further, the failure impact component is analyzed and the diagnose result is obtained. The analysis results of simulation signal and application examples confirm the feasibility and validity of this method, which provides a new idea for the mechanical fault signal extraction in strong noise environment.