The noise identification model of the neural networks is established for the 63SCY14 IB hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully ca...The noise identification model of the neural networks is established for the 63SCY14 IB hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks, The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks, Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump.展开更多
A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectivel...A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectively distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters in time domain are defined for reflecting the features of time signals measured for the fault diagnosis of rotating machinery. The synthetic detection index is also proposed to evaluate the sensitivity of non-dimensional symptom parameters for detecting faults. The practical example of condition diagnosis for detecting and distinguishing fault states of a centrifugal pump system, such as cavitation, impeller eccentricity which often occur in a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.展开更多
Inspired by the modulation mechanism of neuroendocrine-immune system(NEIs),a novel structure of artificial neural network(ANN) named NEI-NN and its learning method are presented.The NEI-NN includes two parts,i.e.,posi...Inspired by the modulation mechanism of neuroendocrine-immune system(NEIs),a novel structure of artificial neural network(ANN) named NEI-NN and its learning method are presented.The NEI-NN includes two parts,i.e.,positive subnetwork(PSN) and negative sub-network(NSN).The neuron functions of PSN and NSN are designed according to the increased and decreased secretion functions of hormone,respectively.In order to make the novel neural network learn quickly,the novel neuron based on some characteristics of NEIs is also redesigned.Besides the normal input signals,two control signals are considered in the proposed solution.One is the enable/disable signal,and the other is the slope control signal.The former can modify the structure of NEI-NN,and the later can regulate the evolutionary speed of NEINN.The NEI-NN can obtain the optimized network structure by using error back-propagation(BP) learning algorithm.Since the modeling of the beam pumping unit is very difficult by using the conventional method,the modeling of bean bump unit is chosen to examine the performance of the NEI-NN.The experiment results show that the optimized structure and learning speed of NEI-NN are better than those of the conventional neural network.展开更多
Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to impro...Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm（MOGA） and artificial neural networks（ANN） for a double-channel pump＇s impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vortex and intensity of vortex decreases in the whole flow channel. This paper provides a promising tool to solve the hydraulic optimization problem of pumps＇ impellers.展开更多
基金This project is supported by National Natural Science Foundation of China (No.50175097).
文摘The noise identification model of the neural networks is established for the 63SCY14 IB hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks, The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks, Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump.
基金Sci-Tech Planning Projects of Chongqing City,China(No.CSTC2007AA7003).
文摘A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectively distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters in time domain are defined for reflecting the features of time signals measured for the fault diagnosis of rotating machinery. The synthetic detection index is also proposed to evaluate the sensitivity of non-dimensional symptom parameters for detecting faults. The practical example of condition diagnosis for detecting and distinguishing fault states of a centrifugal pump system, such as cavitation, impeller eccentricity which often occur in a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.
基金the Key Project of the National Natural Science Foundation of China(No.61134009)National Natural Science Foundations of China(Nos.61473078,61271001)+5 种基金Program for Changjiang Scholars from the Ministry of Education,ChinaSpecialized Research Fund for Shanghai Leading Talents,ChinaProject of the Shanghai Committee of Science and Technology,China(No.13JC1407500)the Fundamental Research Funds for the Central Universities,China(No.09CX04026A)Excellent Youth and Middle Age Scientists Fund of Shandong Province,China(No.BS2010DX038)Fundamental Research Funds for the Central Universities,China(No.14CX02171A)
文摘Inspired by the modulation mechanism of neuroendocrine-immune system(NEIs),a novel structure of artificial neural network(ANN) named NEI-NN and its learning method are presented.The NEI-NN includes two parts,i.e.,positive subnetwork(PSN) and negative sub-network(NSN).The neuron functions of PSN and NSN are designed according to the increased and decreased secretion functions of hormone,respectively.In order to make the novel neural network learn quickly,the novel neuron based on some characteristics of NEIs is also redesigned.Besides the normal input signals,two control signals are considered in the proposed solution.One is the enable/disable signal,and the other is the slope control signal.The former can modify the structure of NEI-NN,and the later can regulate the evolutionary speed of NEINN.The NEI-NN can obtain the optimized network structure by using error back-propagation(BP) learning algorithm.Since the modeling of the beam pumping unit is very difficult by using the conventional method,the modeling of bean bump unit is chosen to examine the performance of the NEI-NN.The experiment results show that the optimized structure and learning speed of NEI-NN are better than those of the conventional neural network.
基金Supported by National Natural Science Foundation of China(Grant No.51109094)Priority Academic Program Development of Jiangsu Higher Education Institutions of China
文摘Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm（MOGA） and artificial neural networks（ANN） for a double-channel pump＇s impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vortex and intensity of vortex decreases in the whole flow channel. This paper provides a promising tool to solve the hydraulic optimization problem of pumps＇ impellers.
