风电机组的偏航轴承和变桨轴承、航天发射塔架的回转支承轴承、起重机和挖掘机的转盘轴承等,都具有低速往复运转的特点。低速往复运转轴承的故障诊断极具挑战:低速工况下损伤接触的冲击力小,损伤冲击信号弱;减速换向冲击信号对故障冲击...风电机组的偏航轴承和变桨轴承、航天发射塔架的回转支承轴承、起重机和挖掘机的转盘轴承等,都具有低速往复运转的特点。低速往复运转轴承的故障诊断极具挑战:低速工况下损伤接触的冲击力小,损伤冲击信号弱;减速换向冲击信号对故障冲击信号的干扰大;覆盖多个往复运转行程的长信号不具有周期性,等等。为了解决上述问题,提出一种基于调制信号双谱(Modulation Signal Bispectrum,MSB)切片总体平均的低速往复运转轴承故障诊断方法。首先,利用转速跟踪过零点对振动信号进行信号重采样处理,并依据编码器信号从重采样信号中分离出单个行程的短信号集合;然后,对每一个短信号进行MSB分析,生成MSB的载波切片谱,根据载波切片谱寻找最优载波频率及其对应的调制信号切片谱;最后,对短信号集合的MSB调制信号切片谱进行总体平均,生成切片谱总体平均特征。故障试验数据验证结果表明,MSB切片总体平均特征能够有效诊断低速往复运转轴承的故障。展开更多
Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous...Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous operation bearings, such as yaw bearings and pitch bearings in wind turbines, and rotating support bearings in space launch towers, presents more challenges compared to continuous rolling bearings. Firstly, these bearings have very slow speeds, resulting in weak collected fault signals that are heavily masked by severe noise interference. Secondly, their limited rotational angles during operation lead to a restricted number of fault signals. Lastly, the interference from deceleration and direction-changing impact signals significantly affects fault impact signals. To address these challenges, this paper proposes a method for extracting fault features in low-speed reciprocating bearings based on short signal segmentation and modulation signal bispectrum (MSB) slicing. This method initially separates short signals corresponding to individual cycles from the vibration signals based on encoder signals. Subsequently, MSB analysis is performed on each short signal to generate MSB carrier-slice spectra. The optimal carrier frequency and its corresponding modulation signal slice spectrum are determined based on the carrier-slice spectra. Finally, the MSB modulation signal slice spectra of the short signal set are averaged to obtain the overall average feature of the sliced spectra.展开更多
The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extra...The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD（empirical mode decomposition） and EEMD（ensemble empirical mode decomposition） are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM（support vector machine）, extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD＋SVM method is compared with the EEMD＋SVM and EMD＋SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.展开更多
文摘风电机组的偏航轴承和变桨轴承、航天发射塔架的回转支承轴承、起重机和挖掘机的转盘轴承等,都具有低速往复运转的特点。低速往复运转轴承的故障诊断极具挑战:低速工况下损伤接触的冲击力小,损伤冲击信号弱;减速换向冲击信号对故障冲击信号的干扰大;覆盖多个往复运转行程的长信号不具有周期性,等等。为了解决上述问题,提出一种基于调制信号双谱(Modulation Signal Bispectrum,MSB)切片总体平均的低速往复运转轴承故障诊断方法。首先,利用转速跟踪过零点对振动信号进行信号重采样处理,并依据编码器信号从重采样信号中分离出单个行程的短信号集合;然后,对每一个短信号进行MSB分析,生成MSB的载波切片谱,根据载波切片谱寻找最优载波频率及其对应的调制信号切片谱;最后,对短信号集合的MSB调制信号切片谱进行总体平均,生成切片谱总体平均特征。故障试验数据验证结果表明,MSB切片总体平均特征能够有效诊断低速往复运转轴承的故障。
文摘Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous operation bearings, such as yaw bearings and pitch bearings in wind turbines, and rotating support bearings in space launch towers, presents more challenges compared to continuous rolling bearings. Firstly, these bearings have very slow speeds, resulting in weak collected fault signals that are heavily masked by severe noise interference. Secondly, their limited rotational angles during operation lead to a restricted number of fault signals. Lastly, the interference from deceleration and direction-changing impact signals significantly affects fault impact signals. To address these challenges, this paper proposes a method for extracting fault features in low-speed reciprocating bearings based on short signal segmentation and modulation signal bispectrum (MSB) slicing. This method initially separates short signals corresponding to individual cycles from the vibration signals based on encoder signals. Subsequently, MSB analysis is performed on each short signal to generate MSB carrier-slice spectra. The optimal carrier frequency and its corresponding modulation signal slice spectrum are determined based on the carrier-slice spectra. Finally, the MSB modulation signal slice spectra of the short signal set are averaged to obtain the overall average feature of the sliced spectra.
基金Supported by National Natural Science Foundation of China(Grant Nos.51175511,61472444)Jiangsu Provincial Natural Science Foundation of China(Grant No.BK20150724)Pre-study Foundation of PLA University of Science and Technology,China(Grant No.KYGYZL139)
文摘The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD（empirical mode decomposition） and EEMD（ensemble empirical mode decomposition） are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM（support vector machine）, extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD＋SVM method is compared with the EEMD＋SVM and EMD＋SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.
