Fault prediction technology of running state of electromechanical systems is one of the key technologies that ensure safe and reliable operation of electromechanical equipment in health state. For multiple types of mo...Fault prediction technology of running state of electromechanical systems is one of the key technologies that ensure safe and reliable operation of electromechanical equipment in health state. For multiple types of modern, high-end and key electromechanical equipment, this paper will describe the early faults prediction method for multi-type electromechanical systems, which is favorable for predicting early faults of complex electromechanical systems in non-stationary, nonlinear, variable working conditions and long-time running state; the paper shall introduce the reconfigurable integration technology of series safety monitoring systems based on which the integrated development platform of series safety monitoring systems is built. This platform can adapt to integrated R&D of series safety monitoring systems characterized by high technology, multiple species and low volume. With the help of this platform, series safety monitoring systems were developed, and the Remote Network Security Monitoring Center for Facility Groups was built. Experimental research and engineering applications show that: this new fault prediction method has realized the development trend features extraction of typical electromechanical systems, multi-information fusion, intelligent information decision-making and so on, improving the processing accuracy, relevance and applicability of information; new reconfigurable integration technologies have improved the integration level and R&D efficiency of series safety monitoring systems as well as expanded the scope of application; the series safety monitoring systems developed based on reconfigurable integration platform has already played an important role in many aspects including ensuring safety operation of equipment, stabilizing product quality, optimizing running state, saving energy consumption, reducing environmental pollution, improving working conditions, carrying out scientific maintenance, advancing equipment utilization, saving maintenance charge and enhancing the level of information management.展开更多
Flue gas generator set is a kind of large high-speed rotating machinery in petrochemical industry.This research focuses on noise reduction algorithms basis ontheBirgé-Massartthreshold.Obtained the threshold throu...Flue gas generator set is a kind of large high-speed rotating machinery in petrochemical industry.This research focuses on noise reduction algorithms basis ontheBirgé-Massartthreshold.Obtained the threshold through Penalization Strategy Provided by Birgé-Massart;constructed different modulus maximum vertex neighborhood on different wavelet transform decomposition scales to influence the search process of modulus maximum point;obtained the appropriate modulus maximum points sequence on various wavelet decomposition scales;highlighted state feature information;finally usedMallat staggered projection to reconstruct signals.In order to validate the effectiveness of the algorithm,it was compared with four kinds of threshold noise suppression methods namely Rigrsure,Sqtwolog,Heursure,Minimaxi.The results show that this algorithm has a better signal to noise ratio and mean-square error.展开更多
Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold l...Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine(PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.展开更多
基金Supported by National Natural Science Fund Project(51275052)Key project supported by Beijing Municipal Natural Science Foundation(3131002)Open topic of Key Laboratory of Key Laboratory of Modern Measurement & Control Technology,Ministry of Education(KF20141123202,KF20111123201)
文摘Fault prediction technology of running state of electromechanical systems is one of the key technologies that ensure safe and reliable operation of electromechanical equipment in health state. For multiple types of modern, high-end and key electromechanical equipment, this paper will describe the early faults prediction method for multi-type electromechanical systems, which is favorable for predicting early faults of complex electromechanical systems in non-stationary, nonlinear, variable working conditions and long-time running state; the paper shall introduce the reconfigurable integration technology of series safety monitoring systems based on which the integrated development platform of series safety monitoring systems is built. This platform can adapt to integrated R&D of series safety monitoring systems characterized by high technology, multiple species and low volume. With the help of this platform, series safety monitoring systems were developed, and the Remote Network Security Monitoring Center for Facility Groups was built. Experimental research and engineering applications show that: this new fault prediction method has realized the development trend features extraction of typical electromechanical systems, multi-information fusion, intelligent information decision-making and so on, improving the processing accuracy, relevance and applicability of information; new reconfigurable integration technologies have improved the integration level and R&D efficiency of series safety monitoring systems as well as expanded the scope of application; the series safety monitoring systems developed based on reconfigurable integration platform has already played an important role in many aspects including ensuring safety operation of equipment, stabilizing product quality, optimizing running state, saving energy consumption, reducing environmental pollution, improving working conditions, carrying out scientific maintenance, advancing equipment utilization, saving maintenance charge and enhancing the level of information management.
基金partially supported by The National Natural Science Foundation of China(51275052)Natural Science Foundation of Beijing(3131002)+1 种基金The Key Project Supported by Introduction of Foreign Talents and Technologies of Beijing(B201101010)The major projects of the National Social Science Fund(12&ZD234)
文摘Flue gas generator set is a kind of large high-speed rotating machinery in petrochemical industry.This research focuses on noise reduction algorithms basis ontheBirgé-Massartthreshold.Obtained the threshold through Penalization Strategy Provided by Birgé-Massart;constructed different modulus maximum vertex neighborhood on different wavelet transform decomposition scales to influence the search process of modulus maximum point;obtained the appropriate modulus maximum points sequence on various wavelet decomposition scales;highlighted state feature information;finally usedMallat staggered projection to reconstruct signals.In order to validate the effectiveness of the algorithm,it was compared with four kinds of threshold noise suppression methods namely Rigrsure,Sqtwolog,Heursure,Minimaxi.The results show that this algorithm has a better signal to noise ratio and mean-square error.
基金Beijing Natural Science Foundation(KZ201211232039)National Natural Science Foundation of China(51275052)+1 种基金Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipalipality(PHR201106132)PXM2014_014224_000080
文摘Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine(PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.
