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
In order to meet the increasingly stringent requirements for nitrogen oxides(NOx)emissions from gas boilers,flue gas recirculation(FGR)technology is commonly used to achieve ultra-low NOx emissions.However,under some ...In order to meet the increasingly stringent requirements for nitrogen oxides(NOx)emissions from gas boilers,flue gas recirculation(FGR)technology is commonly used to achieve ultra-low NOx emissions.However,under some ultra-low NOx combustion conditions with FGR,a surge phenomenon occurs in the boiler,which causes a flameout and should be avoided.In this study,the diffusion combustion surge of gas boiler with a rated power of 350 k W and equipped with FGR device was investigated.Pressure characteristic analysis results of the initial process of combustion surge showed that the high-frequency component of pressure is closely related to combustion stability and its change can provide reference for the occurrence of surge.Besides,the initial process of surge was analyzed by wavelet packet entropy method.Results indicated that the wavelet packet entropy of pressure signals could effectively reflect the stability of combustion in the furnace,and it could also be used to study the occurrence of surge.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.
基金supported by the National Natural Science Foundation of China(51976140)the National Key Research and Development Program of China(2017YFF0209801)。
文摘In order to meet the increasingly stringent requirements for nitrogen oxides(NOx)emissions from gas boilers,flue gas recirculation(FGR)technology is commonly used to achieve ultra-low NOx emissions.However,under some ultra-low NOx combustion conditions with FGR,a surge phenomenon occurs in the boiler,which causes a flameout and should be avoided.In this study,the diffusion combustion surge of gas boiler with a rated power of 350 k W and equipped with FGR device was investigated.Pressure characteristic analysis results of the initial process of combustion surge showed that the high-frequency component of pressure is closely related to combustion stability and its change can provide reference for the occurrence of surge.Besides,the initial process of surge was analyzed by wavelet packet entropy method.Results indicated that the wavelet packet entropy of pressure signals could effectively reflect the stability of combustion in the furnace,and it could also be used to study the occurrence of surge.
