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A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

《机械工程前沿（英文）》 2023年 第18卷 第2期 doi: 10.1007/s11465-022-0736-9

摘要： Recently, advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate diagnosis results, multi-sensor fusion has long been studied in the fault diagnosis field. However, existing studies suffer from two weaknesses. First, the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types. Second, the localization for multi-source faults is seldom investigated, although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable. This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations (MSRs). First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition results. Second, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault sources are therefore determined. The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump. Results show the proposed method’s validity in diagnosing fault types and sources.

关键词： fault recognition     fault localization     multi-sensor relations     network analysis     graph neural network    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

《能源前沿（英文）》 doi: 10.1007/s11708-023-0891-7

摘要： As the intersection of disciplines deepens, the field of battery modeling is increasingly employing various artificial intelligence (AI) approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation. This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning (ML), one of the many branches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neural network (NN) methods in ML for lithium-ion battery SOH simulation and prediction. NN is one of the important branches of ML, in which the application of NNs such as backpropagation NN, convolutional NN, and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention. Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency, low energy consumption, high robustness, and scalable models. In the future, NN can make a greater contribution to lithium-ion battery management by, first, utilizing more field data to play a more practical role in health feature screening and model building, and second, by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent. The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science, reliability, stability, and robustness of lithium-ion battery management.

关键词： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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受限空间火灾模型研究进展

郑昕,袁宏永

《中国工程科学》 2004年 第6卷 第3期   页码 68-74

摘要：

火灾模型是从工程科学的角度出发，分析研究火灾的发生、发展，烟气蔓延以及火灾对周围环境诸如建筑设备、森林植被及大气环境等影响的数学模型。介绍了广泛应用于建筑物内部受限空间的场、区域、网模型以及经验模型的理论思想与数学方程，分析了4种模型在相应环境下应用的合理性，并对火灾模型的发展做出了展望。

关键词： 受限空间     场模型     区域模型     网模型     场区网模型     经验模型    

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A constrained neural network model for soil liquefaction assessment with global applicability

Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG

《结构与土木工程前沿（英文）》 2020年 第14卷 第5期   页码 1066-1082 doi: 10.1007/s11709-020-0651-2

摘要： A constrained back propagation neural network (C-BPNN) model for standard penetration test based soil liquefaction assessment with global applicability is developed, incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships. For its development and validation, a comprehensive liquefaction data set is compiled, covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries. The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints, input data selection, and computation and calibration procedures. Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model, and are thus adopted as constraints for the C-BPNN model. The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice. The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.

关键词： soil liquefaction assessment     case history dataset     constrained neural network model     existing knowledge    

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A novel flow-resistor network model for characterizing enhanced geothermal system heat reservoir

Jian GUO, Wenjiong CAO, Yiwei WANG, Fangming JIANG

《能源前沿（英文）》 2019年 第13卷 第1期   页码 99-106 doi: 10.1007/s11708-018-0555-1

摘要： The fracture characteristics of a heat reservoir are of critical importance to enhanced geothermal systems, which can be investigated by theoretical modeling. This paper presents the development of a novel flow-resistor network model to describe the hydraulic processes in heat reservoirs. The fractures in the reservoir are simplified by using flow resistors and the typically complicated fracture network of the heat reservoir is converted into a flow-resistor network with a reasonably simple pattern. For heat reservoirs with various fracture configurations, the corresponding flow-resistor networks are identical in terms of framework though the networks may have different section numbers and the flow resistors may have different values. In this paper, numerous cases of different section numbers and resistor values are calculated and the results indicate that the total number of flow resistances between the injection and production wells is primarily determined by the number of fractures in the reservoir. It is also observed that a linear dependence of the total flow resistance on the number of fractures and the relation is obtained by the best fit of the calculation results. Besides, it performs a case study dealing with the Soultz enhanced geothermal system (EGS). In addition, the fracture numbers underneath specific well systems are derived. The results provide insight on the tortuosity of the flow path between different wells.

关键词： enhanced geothermal systems     flow-resistor network model     fracture characteristics     heat reservoir    

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

《结构与土木工程前沿（英文）》 2020年 第14卷 第6期   页码 1285-1298 doi: 10.1007/s11709-020-0691-7

摘要： Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.

关键词： multiscale method     constitutive model     feedforward neural network     recurrent neural network    

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Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

《结构与土木工程前沿（英文）》 2019年 第13卷 第3期   页码 674-685 doi: 10.1007/s11709-018-0505-3

摘要： M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups. Pile length ( ), angle of oblique load ( ), sand density ( ), number of batter piles ( ), and number of vertical piles ( ) as input and oblique load ( ) as output was used. Results suggest improved performance by RF regression for both pile groups. M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also. Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data. NN based approach was found performing equally well with both smooth and rough piles. Sensitivity analysis using all three modelling approaches suggest angle of oblique load ( ) and number of batter pile ( ) affect the oblique load capacity for both smooth and rough pile groups.

关键词： batter piles     oblique load test     neural network     M5 model tree     random forest regression     ANOVA    

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Multi-class dynamic network traffic flow propagation model with physical queues

Yanfeng LI, Jun LI

《工程管理前沿（英文）》 2017年 第4卷 第4期   页码 399-407 doi: 10.15302/J-FEM-2017041

摘要： This paper proposes an improved multi-class dynamic network traffic flow propagation model with a consideration of physical queues. Each link is divided into two areas: Free flow area and queue area. The vehicles of the same class are assumed to satisfy the first-in-first-out (FIFO) principle on the whole link, and the vehicles of the different classes also follow FIFO in the queue area but not in the free flow area. To characterize this phenomenon by numerical methods, the improved model is directly formulated in discrete time space. Numerical examples are developed to illustrate the unrealistic flows of the existing model and the performance of the improved model. This analysis can more realistically capture the traffic flow propagation, such as interactions between multi-class traffic flows, and the dynamic traffic interactions across multiple links.

关键词： first-in-first-out (FIFO)     multi-class traffic     physical queues     traffic flow modeling    

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Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

《结构与土木工程前沿（英文）》 2022年 第16卷 第10期   页码 1213-1232 doi: 10.1007/s11709-022-0880-7

摘要： The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius rc, unconfined concrete strength fco, thickness nt, the elastic modulus of fiber Ef , the elastic modulus of mortar Em. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available.

关键词： FRCM     deep neural networks     confinement effect     strength model     confined concrete    

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Sustainability performance analysis of environment innovation systems using a two-stage network DEA model

《工程管理前沿（英文）》   页码 425-438 doi: 10.1007/s42524-022-0205-5

摘要： The term environmental innovation system refers to an innovation network composed of enterprises, universities, and research institutions involved in the development and diffusion of environmental technology, with the participation of a government. An environmental innovation system not only exerts important impact on the achievement of carbon neutrality but also affects social and economic activities. Investigations on environmental innovation system performance constantly assume a single-stage independent system while ignoring its internal structure. However, such systems are composed of environmental innovation research and development (R&D) and environmental innovation conversion subsystems. A two-stage data envelopment analysis (DEA) model is developed in this study to analyze the efficiency of Chinese regional environmental innovation system by opening the “black box” and considering shared resources. Empirical results indicated that China presents high overall environmental innovation efficiency although some regions need to improve. Regions with low efficiencies in both environmental innovation R&D (EIR) and environmental innovation conversion (EIC) subsystems should expand their investment in and strengthen the management of environmental innovation resources. Regions with low EIR efficiency should improve the absorption and transformation of environmental innovation achievements. Regions with low EIC efficiency should increase investment in the commercialization of environmental innovation achievements and encourage green economy industries, such as new energy, art, tourism, and environmental protection.

关键词： data envelopment analysis     environmental efficiency     environmental innovation system     shared resources     two-stage structure    

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Hydraulic model for multi-sources reclaimed water pipe network based on EPANET and its applications in

JIA Haifeng, WEI Wei, XIN Kunlun

《环境科学与工程前沿（英文）》 2008年 第2卷 第1期   页码 57-62 doi: 10.1007/s11783-008-0013-0

摘要： Water shortage is one of the major water related problems for many cities in the world. The planning for utilization of reclaimed water has been or would be drafted in these cities. For using the reclaimed water soundly, Beijing planned to build a large scale reclaimed water pipe networks with multi-sources. In order to support the plan, the integrated hydraulic model of planning pipe network was developed based on EPANET supported by geographic information system (GIS). The complicated pipe network was divided into four weak conjunction subzones according to the distribution of reclaimed water plants and the elevation. It could provide a better solution for the problem of overhigh pressure in several regions of the network. Through the scenarios analysis in different subzones, some of the initial diameter of pipes in the network was adjusted. At last the pipe network planning scheme of reclaimed water was proposed. The proposed planning scheme could reach the balances between reclaimed water requirements and reclaimed water supplies, and provided a scientific basis for the reclaimed water utilization in Beijing. Now the scheme had been adopted by Beijing municipal government.

关键词： diameter     Beijing municipal     reclaimed     planning     elevation    

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An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

《结构与土木工程前沿（英文）》 2020年 第14卷 第6期   页码 1299-1315 doi: 10.1007/s11709-020-0712-6

摘要： The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers.

关键词： artificial neural network     hybrid fiber reinforced concrete     tensile behavior     sensitivity analysis     stress-strain curve    

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A modified zone model for estimating equivalent room thermal capacity

Hua CHEN, Xiaolin WANG

《能源前沿（英文）》 2013年 第7卷 第3期   页码 351-357 doi: 10.1007/s11708-013-0254-x

摘要： The zone model has been widely applied in control analysis of heating, ventilation and air conditioning (HVAC) systems to achieve a high building efficiency. This paper proposed a modified zone model which is much simpler in the HVAC system simulation and has the similar accuracy to the complicated simulation model. The proposed model took into consideration the effect of envelop heat reservoir on the room indoor temperature by introducing the thermal admittance of the inner surfaces of the building enclosure. The thermal admittance for the building enclosure was developed based on the building thermal network analytical theory and transfer function method. The efficacy of the proposed model was demonstrated by comparing it with the complicated model — heat balance method (HTB2 program). The predicted results from the proposed model well agreed with those from the complicated simulation. The proposed model can then make the HVAC system dynamic simulation much faster and more acceptable for control design due to its simplicity and efficiency.

关键词： room model     thermal network analysis     transfer function     heating     ventilation and air conditioning (HVAC) system simulation    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年 第16卷 第3期   页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost     deep neural network     healthcare     risk prediction    

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马尔可夫网络排队模型在电梯配置中的应用

宗群,程义菊,宋军远

《中国工程科学》 2003年 第5卷 第10期   页码 69-72

摘要：

利用马尔可夫网络排队理论建立了电梯交通模型，在此基础上对各服务站的电梯配置交通进行计算，得到电梯的配置参数。通过与传统的电梯配置方法相比较，显示出明显的优越性，也表明了利用马尔可夫网络排队模型进行电梯配置的有效性和可行性。

关键词： 马尔可夫网络排队理论     电梯交通模型     优化电梯配置    

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标题 作者 时间 类型 操作

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

期刊论文

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

期刊论文

受限空间火灾模型研究进展

郑昕,袁宏永

期刊论文

A constrained neural network model for soil liquefaction assessment with global applicability

Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG

期刊论文

A novel flow-resistor network model for characterizing enhanced geothermal system heat reservoir

Jian GUO, Wenjiong CAO, Yiwei WANG, Fangming JIANG

期刊论文

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

期刊论文

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

期刊论文

Multi-class dynamic network traffic flow propagation model with physical queues

Yanfeng LI, Jun LI

期刊论文

Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

期刊论文

Sustainability performance analysis of environment innovation systems using a two-stage network DEA model

期刊论文

Hydraulic model for multi-sources reclaimed water pipe network based on EPANET and its applications in

JIA Haifeng, WEI Wei, XIN Kunlun

期刊论文

An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

期刊论文

A modified zone model for estimating equivalent room thermal capacity

Hua CHEN, Xiaolin WANG

期刊论文

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

期刊论文

马尔可夫网络排队模型在电梯配置中的应用

宗群,程义菊,宋军远

期刊论文