Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonline...Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonlinearity,leading to delays in detecting time-varying data features.Additionally,the uncertain kernel function and kernel parameters limit the ability of the extracted features to express process characteristics,resulting in poor fault detection performance.To alleviate the above problems,a novel randomized auto-regressive dynamic slow feature analysis(RRDSFA)method is proposed to simultaneously monitor the operating point deviations and process dynamic faults,enabling real-time monitoring of data features in industrial processes.Firstly,the proposed Random Fourier mappingbased method achieves more effective nonlinear transformation,contrasting with the current kernelbased RDSFA algorithm that may lead to significant computational complexity.Secondly,a randomized RDSFA model is developed to extract nonlinear dynamic slow features.Furthermore,a Bayesian inference-based overall fault monitoring model including all RRDSFA sub-models is developed to overcome the randomness of random Fourier mapping.Finally,the superiority and effectiveness of the proposed monitoring method are demonstrated through a numerical case and a simulation of continuous stirred tank reactor.展开更多
To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved...To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.展开更多
Recent studies have pointed out that the widespread iron deposits in southwestern Fujian metallogenic belt(SFMB)(China) are skarn-type deposits associated with the Yanshanian granites. There is still excellent potenti...Recent studies have pointed out that the widespread iron deposits in southwestern Fujian metallogenic belt(SFMB)(China) are skarn-type deposits associated with the Yanshanian granites. There is still excellent potential for mineral exploration because large areas in this belt are covered by forest. A new predictive model for mapping skarn-type Fe deposit prospectivity in this belt was developed and focused on in this study, using five criteria as evidence:(1) the contact zones of Yanshanian granites(GRANITE);(2) the contact zones within the late Paleozoic marine sedimentary rocks and the carbonate formations(FORMATION);(3) the NE-NNE-trending faults(FAULT);(4) the zones of skarn alterations(SKARN); and(5) the aeromagnetic anomaly(AEROMAGNETIC). The fuzzy weights of evidence(FWof E) method, developed from the classical weights of evidence(Wof E) and based on fuzzy sets and fuzzy probabilities, could provide smaller variances and more accurate posterior probabilities and could effectively minimize the uncertainty caused by omitted or wrongly assigned data and be more flexible than the Wof E. It is an efficient and widely used method for mineral potential mapping. Random forests(RF) is a new and useful method for data-driven predictive mapping of mineral prospectivity method, and needs further scrutiny. Both prospectivity results respectively using the FWof E and RF methods reveal that the prediction model for the skarn-type Fe deposits in the SFMB is successful and efficient. Both methods suggested that the GRANITE and FORMATION are the most valuable evidence maps, followed by SKARN, AEROMAGNETIC, and FAULT. This is coincident with the skarn-type Fe deposit mineral model in the SFMB. The unstable performance experienced when FORMATION was omitted might indicate that the highest uncertainty and risk in follow-up exploration is related to the sequences. In addition, the performance of the RF method for the skarn-type Fe deposits prospectivity in the SFMB is better than the FWof E; therefore, it could be used to guide further exploration of skarn-type Fe prospects in the SFMB.展开更多
基金supported by the Program of National Natural Science Foundation of China(U23A20329,62163036)Youth Academic and Technical Leaders Reserve Talent Training project(202105AC160094)Industrial Innovation Talent Special Project of Xingdian Talent Support Program(XDYC-CYCX-2022-0010).
文摘Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonlinearity,leading to delays in detecting time-varying data features.Additionally,the uncertain kernel function and kernel parameters limit the ability of the extracted features to express process characteristics,resulting in poor fault detection performance.To alleviate the above problems,a novel randomized auto-regressive dynamic slow feature analysis(RRDSFA)method is proposed to simultaneously monitor the operating point deviations and process dynamic faults,enabling real-time monitoring of data features in industrial processes.Firstly,the proposed Random Fourier mappingbased method achieves more effective nonlinear transformation,contrasting with the current kernelbased RDSFA algorithm that may lead to significant computational complexity.Secondly,a randomized RDSFA model is developed to extract nonlinear dynamic slow features.Furthermore,a Bayesian inference-based overall fault monitoring model including all RRDSFA sub-models is developed to overcome the randomness of random Fourier mapping.Finally,the superiority and effectiveness of the proposed monitoring method are demonstrated through a numerical case and a simulation of continuous stirred tank reactor.
基金by National Natural Science Foundation of China(62373142,62033014)Natural Science Foundation of Hunan Province(2025JJ70017,2022JJ50074).
文摘To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.
基金the joint financial support from a research project on "Quantitative models for prediction of strategic mineral resources in China" (Grant No. 201211022) by China Geological Surveythe National Natural Science Foundation of China (Grant Nos. 41372007, 41430320 & 41522206)the Program for New Century Excellent Talents in University (Grant No. NCET-13-1016)
文摘Recent studies have pointed out that the widespread iron deposits in southwestern Fujian metallogenic belt(SFMB)(China) are skarn-type deposits associated with the Yanshanian granites. There is still excellent potential for mineral exploration because large areas in this belt are covered by forest. A new predictive model for mapping skarn-type Fe deposit prospectivity in this belt was developed and focused on in this study, using five criteria as evidence:(1) the contact zones of Yanshanian granites(GRANITE);(2) the contact zones within the late Paleozoic marine sedimentary rocks and the carbonate formations(FORMATION);(3) the NE-NNE-trending faults(FAULT);(4) the zones of skarn alterations(SKARN); and(5) the aeromagnetic anomaly(AEROMAGNETIC). The fuzzy weights of evidence(FWof E) method, developed from the classical weights of evidence(Wof E) and based on fuzzy sets and fuzzy probabilities, could provide smaller variances and more accurate posterior probabilities and could effectively minimize the uncertainty caused by omitted or wrongly assigned data and be more flexible than the Wof E. It is an efficient and widely used method for mineral potential mapping. Random forests(RF) is a new and useful method for data-driven predictive mapping of mineral prospectivity method, and needs further scrutiny. Both prospectivity results respectively using the FWof E and RF methods reveal that the prediction model for the skarn-type Fe deposits in the SFMB is successful and efficient. Both methods suggested that the GRANITE and FORMATION are the most valuable evidence maps, followed by SKARN, AEROMAGNETIC, and FAULT. This is coincident with the skarn-type Fe deposit mineral model in the SFMB. The unstable performance experienced when FORMATION was omitted might indicate that the highest uncertainty and risk in follow-up exploration is related to the sequences. In addition, the performance of the RF method for the skarn-type Fe deposits prospectivity in the SFMB is better than the FWof E; therefore, it could be used to guide further exploration of skarn-type Fe prospects in the SFMB.
