The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condi...The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condition and fault type but also the severity of the fault. This means fault severity quantitative analysis is one of most active and valid ways to realize proper maintenance decision. Aiming at the deficiency of the research in bearing single point pitting fault quantitative diagnosis, a new back-propagation neural network method based on wavelet packet decomposition coefficient entropy is proposed. The three levels of wavelet packet coefficient entropy（WPCE） is introduced as a characteristic input vector to the BPNN. Compared with the wavelet packet decomposition energy ratio input vector, WPCE shows more sensitive in distinguishing from the different fault severity degree of the measured signal. The engineering application results show that the quantitative trend fault diagnosis is realized in the different fault degree of the single point bearing pitting fault. The breakthrough attempt from quantitative to qualitative on the pattern recognition of rolling element bearings fault diagnosis is realized.展开更多
针对强背景噪声及谐波干扰的滚动轴承早期微弱故障特征提取,提出一种改进奇异值分解(Improved singular value decomposition,ISVD)的故障诊断新方法。首先,针对正弦信号、复合正弦信号和周期性冲击信号各自特征,根据奇异值子对(Singula...针对强背景噪声及谐波干扰的滚动轴承早期微弱故障特征提取,提出一种改进奇异值分解(Improved singular value decomposition,ISVD)的故障诊断新方法。首先,针对正弦信号、复合正弦信号和周期性冲击信号各自特征,根据奇异值子对(Singular value pairs,SVP)的形成原理,分别提出改进的Hankel矩阵嵌入维数优化选取原则,明确了该参数的量化范围,进而确定奇异值分解(Singular value decomposition,SVD)的最佳嵌入维数。该算法可自适应匹配SVD的Hankel矩阵最佳嵌入维数,进而获得形成SVP分布的信号分解策略。随后,结合谐波干扰的能量及SVP分布,实现对包含轴承微弱故障成分的子信号进行定位。最后,采用反对角线平均法重构目标子信号,对其进行包络谱分析获得诊断结果。仿真的滚动轴承故障信号和多组试验信号分析验证了所提方法的可行性和有效性。展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51175007,51075023)
文摘The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condition and fault type but also the severity of the fault. This means fault severity quantitative analysis is one of most active and valid ways to realize proper maintenance decision. Aiming at the deficiency of the research in bearing single point pitting fault quantitative diagnosis, a new back-propagation neural network method based on wavelet packet decomposition coefficient entropy is proposed. The three levels of wavelet packet coefficient entropy（WPCE） is introduced as a characteristic input vector to the BPNN. Compared with the wavelet packet decomposition energy ratio input vector, WPCE shows more sensitive in distinguishing from the different fault severity degree of the measured signal. The engineering application results show that the quantitative trend fault diagnosis is realized in the different fault degree of the single point bearing pitting fault. The breakthrough attempt from quantitative to qualitative on the pattern recognition of rolling element bearings fault diagnosis is realized.
文摘针对强背景噪声及谐波干扰的滚动轴承早期微弱故障特征提取,提出一种改进奇异值分解(Improved singular value decomposition,ISVD)的故障诊断新方法。首先,针对正弦信号、复合正弦信号和周期性冲击信号各自特征,根据奇异值子对(Singular value pairs,SVP)的形成原理,分别提出改进的Hankel矩阵嵌入维数优化选取原则,明确了该参数的量化范围,进而确定奇异值分解(Singular value decomposition,SVD)的最佳嵌入维数。该算法可自适应匹配SVD的Hankel矩阵最佳嵌入维数,进而获得形成SVP分布的信号分解策略。随后,结合谐波干扰的能量及SVP分布,实现对包含轴承微弱故障成分的子信号进行定位。最后,采用反对角线平均法重构目标子信号,对其进行包络谱分析获得诊断结果。仿真的滚动轴承故障信号和多组试验信号分析验证了所提方法的可行性和有效性。
