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
针对由于实际工况中风电机组轴承发生故障所采得的信号会受到变速变载的影响,造成故障特征难以提取的问题,提出了基于频域能量算子(Frequency domain energy operator,FDEO)与自适应最大2阶循环平稳盲解卷积(Adaptive maximum second or...针对由于实际工况中风电机组轴承发生故障所采得的信号会受到变速变载的影响,造成故障特征难以提取的问题,提出了基于频域能量算子(Frequency domain energy operator,FDEO)与自适应最大2阶循环平稳盲解卷积(Adaptive maximum second order cyclostationarity blind deconvolution,ACYCBD)的风电机组轴承故障特征提取方法。首先,通过SCADA数据提供的高速轴转速平均速度对CMS(Condition monitoring system)系统采集的振动信号进行感兴趣的振动成分选择,并通过窄带滤波和FDEO对振动信号进行瞬时频率估计和阶次跟踪;其次,针对风电机组振源多、振动信号复杂的特点,对通过阶次跟踪后的角度域振动信号应用改进ACYCBD完成故障特征提取。工程应用分析结果表明,该方法能够准确有效地实现风电机组轴承特征的提取而不受到其他振源的影响。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.
文摘针对由于实际工况中风电机组轴承发生故障所采得的信号会受到变速变载的影响,造成故障特征难以提取的问题,提出了基于频域能量算子(Frequency domain energy operator,FDEO)与自适应最大2阶循环平稳盲解卷积(Adaptive maximum second order cyclostationarity blind deconvolution,ACYCBD)的风电机组轴承故障特征提取方法。首先,通过SCADA数据提供的高速轴转速平均速度对CMS(Condition monitoring system)系统采集的振动信号进行感兴趣的振动成分选择,并通过窄带滤波和FDEO对振动信号进行瞬时频率估计和阶次跟踪;其次,针对风电机组振源多、振动信号复杂的特点,对通过阶次跟踪后的角度域振动信号应用改进ACYCBD完成故障特征提取。工程应用分析结果表明,该方法能够准确有效地实现风电机组轴承特征的提取而不受到其他振源的影响。
