Vibration failure in the pumping system is a significant issue for indus-tries that rely on the pump as a critical device which requires regular maintenance.To save energy and money,a new automated system must be devel...Vibration failure in the pumping system is a significant issue for indus-tries that rely on the pump as a critical device which requires regular maintenance.To save energy and money,a new automated system must be developed that can detect anomalies at an early stage.This paper presents a case study of a machine learning(ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive(VFD).Since the intensity of the vibrational effect depends on which axis has the most significant effect,a three-axis accelerometer is used to measure it in the pumping system.The emphasis is on determining the vibration effect on different axes.For experiment,various ML algorithms are investigated on collected vibratory data through Matlab software in x,y,z axes and performances of the algorithms are compared based on accuracy rate,prediction speed and training time.Based on the proposed research results,the multiclass support vector machine(MSVM)is found to be the best suitable algorithm compared to other algorithms.It has been demonstrated that ML algorithms can detect faults automatically rather than conventional meth-ods.MSVM is used for the proposed work because it is less complex and pro-duces better results with a limited data set.展开更多
The conventional beam pumping unh consumes a large amount of energy due to its unsmooth movement. In this work, we design a new energy-saving parallel four-bar pumping unit and derive the kinematic and dynamic law of ...The conventional beam pumping unh consumes a large amount of energy due to its unsmooth movement. In this work, we design a new energy-saving parallel four-bar pumping unit and derive the kinematic and dynamic law of the drive mechanism systematically by theoretical method. For the given target technical parameter, the theoretical results are verified by computer simulation, which shows that the simulation dynamic curves agree well with the theoretical ones and the calculated power consumption is low. Theoretical analysis shows that the newly designed pumping unit reduces average power by 28.8% compared with its conventional counterpart. The much lower theoretical energy consumption and the better dynamic performance indicate that the new energy-saving pumping unit is well designed and will have a significant application prospect.展开更多
文摘Vibration failure in the pumping system is a significant issue for indus-tries that rely on the pump as a critical device which requires regular maintenance.To save energy and money,a new automated system must be developed that can detect anomalies at an early stage.This paper presents a case study of a machine learning(ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive(VFD).Since the intensity of the vibrational effect depends on which axis has the most significant effect,a three-axis accelerometer is used to measure it in the pumping system.The emphasis is on determining the vibration effect on different axes.For experiment,various ML algorithms are investigated on collected vibratory data through Matlab software in x,y,z axes and performances of the algorithms are compared based on accuracy rate,prediction speed and training time.Based on the proposed research results,the multiclass support vector machine(MSVM)is found to be the best suitable algorithm compared to other algorithms.It has been demonstrated that ML algorithms can detect faults automatically rather than conventional meth-ods.MSVM is used for the proposed work because it is less complex and pro-duces better results with a limited data set.
基金Supported by the Ministry of Science and Technology Innovation Fund for SMEs (09C26214204812)the Venture Capital Fund for SMEs (09C26154204991)
文摘The conventional beam pumping unh consumes a large amount of energy due to its unsmooth movement. In this work, we design a new energy-saving parallel four-bar pumping unit and derive the kinematic and dynamic law of the drive mechanism systematically by theoretical method. For the given target technical parameter, the theoretical results are verified by computer simulation, which shows that the simulation dynamic curves agree well with the theoretical ones and the calculated power consumption is low. Theoretical analysis shows that the newly designed pumping unit reduces average power by 28.8% compared with its conventional counterpart. The much lower theoretical energy consumption and the better dynamic performance indicate that the new energy-saving pumping unit is well designed and will have a significant application prospect.
