Research and design of an expert diagnosis system for rail vehicle driven by data mechanism models

Purpose-The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.Design/methodology/approach-The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle.Based on data mechanism models,it predicts the lifespan of key components,evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.Findings-The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system,which helps operators to monitor the operation of vehicle online,predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.Originality/value-This system improves the efficiency of rail vehicle operation,scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.

Current Situation and Approaches Relating to Construction of Agricultural Production and Operation Development Capacity for Rural Households in Hubei Province

The main body of household operation in the rural household contract responsibility system of our country has developed to the present stage,and has formed the situation that three kinds of rural households coexist,namely,ordinary rural households,major professional households and family farms. The agricultural production and operation of three kinds of household plays an important role in supporting the rapid development of modern agriculture in China. Under the new situation of deepening the rural reform and realizing the goal of well-off society in an all-round way,it is of great practical significance to make a thorough investigation and study on the present situation and approaches relating to the construction of the agricultural production and operation capacity for the three kinds of rural households.

Development and Utilization of the World＇s and China＇s Bulk Mineral Resources and their Supply and Demand Situation in the Next Twenty Years

Bulk mineral resources of iron ores, copper ores, bauxite, lead ores, zinc ores and potassium salt play a pivotal role on the world＇s and China＇s economic development. This study analyzed and predicted their resources base and potential, development and utilization and their world＇s and China＇s supply and demand situation in the future 20 years. The supply and demand of these six bulk mineral products are generally balanced, with a slight surplus, which will guarantee the stability of the international mineral commodity market supply. The six mineral resources （especially iron ores and copper ores） are abundant and have a great potential, and their development and utilization scale will gradually increase. Till the end of 2014, the reserve- production ratio of iron, copper, bauxite, lead, zinc ores and potassium salt was 95 years, 42 years, 100 years, 17 years, 37 years and 170 years, respectively. Except lead ores, the other five types all have reserve-production ratio exceeding 20 years, indicative of a high resources guarantee degree. If the utilization of recycled metals is counted in, the supply of the world＇s six mineral products will exceed the demand in the future twenty years. In 2015-2035, the supply of iron ores, refined copper, primary aluminum, refined lead, zinc and potassium salt will exceed their demand by 0.4-0.7 billion tons （Gt）, 5.0-6.0 million tons （Mt）, 1.1-8.9 Mt, 1.0-2.0 Mt, 1.2-2.0 Mt and 4.8-5.6 Mt, respectively. It is predicted that there is no problem with the supply side of bulk mineral products such as iron ores, but local or structural shortage may occur because of geopolitics, monopoly control, resources nationalism and trade friction. Affected by China＇s compressed industrialized development model, the demand of iron ores （crude steel）, potassium salt, refined lead, refined copper, bauxite （primary aluminum） and zinc will gradually reach their peak in advance. The demand peak of iron ores （crude steel） will reach around 2015, 2016 for potassium salt, 2020 for refined lead, 2021 for bauxite （primary aluminum）, 2022 for refined copper and 2023 for zinc. China＇s demand for iron ores （crude steel）, bauxite （primary aluminum） and zinc in the future 20 years will decline among the world＇s demand, while that for refined copper, refined lead and potassium salt will slightly increase. The demand for bulk mineral products still remains high. In 2015-2035, China＇s accumulative demand for iron ores （crude steel） will be 20.313 Gt （13.429 Gt）, 0.304 Gt for refined copper, 2.466 Gt （0.616 Gt） of bauxite （primary aluminum）, 0.102 Gt of refined lead, 0.138 Gt of zinc and 0.157 Gt of potassium salt, and they account for the world＇s YOY （YOY） accumulative demand of 35.17%, 51.09%, 48.47%, 46.62%, 43.95% and 21.84%, respectively. This proportion is 49.40%, 102.52%, 87.44%, 105.65%, 93.62% and 106.49% of that in 2014, respectively. From the supply side of China＇s bulk mineral resources, it is forecasted that the accumulative supply of primary （mine） mineral products in 2015-2035 is 4.046 Gt of iron ores, 0.591 Gt of copper, 1.129 Gt of bauxite, 63.661 Mt of （mine） lead, 0.109 Gt of （mine） zinc and 0.128 Gt of potassium salt, which accounts for 8.82%, 13.92%, 26.67%, 47.09%, 33.04% and 15.56% of the world＇s predicted YOY production, respectively. With the rapid increase in the smelting capacity of iron and steel and alumina, the rate of capacity utilization for crude steel, refined copper, alumina, primary aluminum and refined lead in 2014 was 72.13%, 83.63%, 74.45%, 70.76% and 72.22%, respectively. During 2000-2014, the rate of capacity utilization for China＇s crude steel and refined copper showed a generally fluctuating decrease, which leads to an insufficient supply of primary mineral products. It is forecasted that the supply insufficiency of iron ores in 2015-2035 is 17.44 Gt, 0.245 Gt of copper in copper concentrates, 1.337 Gt of bauxite, 38.44 Mt of lead in lead concentrates and 29.19 Mt of zinc in zinc concentrates. China has gradually raised the utilization of recycled metals, which has mitigated the insufficient supply of primary metal products to some extent. It is forecasted that in 2015-2035 the accumulative utilization amount of steel scrap （iron ores） is 3.27 Gt （5.08 Gt）, 70.312 Mt of recycled copper, 0.2 Gt of recycled aluminum, 48 Mt of recycled lead and 7.7 Mt of recycled zinc. The analysis on the supply and demand situation of China＇s bulk mineral resources in 2015-2035 suggests that the supply-demand contradiction for these six types of mineral products will decrease, indicative of a generally declining external dependency. If the use of recycled metal amount is counted in, the external dependency of China＇s iron, copper, bauxite, lead, zinc and potassium salt will be 79%, 65%, 26%, 8%, 16% and 18% in 2014, respectively. It is predicted that this external dependency will decrease to 62%, 64%, 20%, -0.93%, 16% and 14% in 2020, respectively, showing an overall decreasing trend. We propose the following suggestions correspondingly. （1） The demand peak of China＇s crude steel and potassium salt will reach during 2015-2023 in succession. Mining transformation should be planned and deployed in advance to deal with the arrival of this demand peak. （2） The supply-demand contradiction of China＇s bulk mineral resources will mitigate in the future 20 years, and the external dependency will decrease accordingly. It is suggested to adjust the mineral resources management policies according to different minerals and regions, and regulate the exploration and development activities. （3） China should further establish and improve the forced mechanism of resolving the smelting overcapacity of steel, refined copper, primary aluminum, lead and zinc to really achieve the goal of ＂reducing excess production capacity＂. （4） In accordance with the national strategic deployment of ＂One Belt One Road＂, China should encourage the excess capacity of steel, copper, alumina and primary aluminum enterprises to transfer to those countries or areas with abundant resources, high energy matching degree and relatively excellent infrastructure. Based on the national conditions, mining condition and geopolitics of the resources countries, we will gradually build steel, copper, aluminum and lead-zinc smelting bases, and potash processing and production bases, which will promote the excess capacity to transfer to the overseas orderly. （5） It is proposed to strengthen the planning and management of renewable resources recycling and to construct industrial base of renewable metal recycling. （6） China should promote the comprehensive development and utilization of paragenetic and associated mineral species to further improve the comprehensive utilization of bulk mineral resources.

A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.

Quantum Operator Model for Data Analysis and Forecast

A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.

LSTM-based lane change prediction using Waymo open motion dataset: The role of vehicle operating space

Lane change prediction is critical for crash avoidance but challenging as it requires the understanding of the instantaneous driving environment.With cutting-edge artificial intelligence and sensing technologies,autonomous vehicles(AVs)are expected to have exceptional perception systems to capture instantaneously their driving environments for predicting lane changes.By exploring the Waymo open motion dataset,this study proposes a framework to explore autonomous driving data and investigate lane change behaviors.In the framework,this study develops a Long Short-Term Memory(LSTM)model to predict lane changing behaviors.The concept of Vehicle Operating Space(VOS)is introduced to quantify a vehicle's instantaneous driving environment as an important indicator used to predict vehicle lane changes.To examine the robustness of the model,a series of sensitivity analysis are conducted by varying the feature selection,prediction horizon,and training data balancing ratios.The test results show that including VOS into modeling can speed up the loss decay in the training process and lead to higher accuracy and recall for predicting lane-change behaviors.This study offers an example along with a methodological framework for transportation researchers to use emerging autonomous driving data to investigate driving behaviors and traffic environments.

Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs

A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out.This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis.The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series.Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting.The analysis of history production data of waterflooding reservoirs shows that,compared with history matching results of numerical reservoir simulation,the production forecasting results from the machine learning model are more accurate,and uncertainty analysis can improve the safety of forecasting results.Furthermore,impulse response analysis can evaluate the oil production contribution of the injection well,which can provide theoretical guidance for adjustment of waterflooding development plan.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

Probabilistic Rationale of Actions for Artificial Intelligence Systems Operating in Uncertainty Conditions

The approach for probabilistic rationale of artificial intelligence systems actions is proposed.It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused on prognostic modeling.The ideas may be applied also by using another probabilistic models which supported by software tools and can predict successfulness or risks on a level of probability distribution functions.The approach includes description of the proposed probabilistic models,optimization methods for rationale actions and incremental algorithms for solving the problems of supporting decision-making on the base of monitored data and rationale robot actions in uncertainty conditions.The approach means practically a proactive commitment to excellence in uncertainty conditions.A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems.

基于SMOTE-IKPCA-SeNet深度迁移学习的小批量生产质量预测研究

随着智能制造技术的发展和客户个性化需求的增加,多品种小批量生产方式逐渐成为制造业的主流。面向大批量生产、以统计过程控制为核心的质量管理方式并不适用于小批量生产。针对复杂生产过程存在参数多、非线性和交互作用的问题,提出利用深度迁移学习的方式将历史生产数据作为源域迁移至小样本目标产品数据进行质量预测。首先,通过合成少数类过采样技术(synthetic minority over-sampling technique,SMOTE)和改进的核主成分分析(improved kernel principal component analysis,IKPCA)算法筛选源域和目标域的可迁移特征,这不仅兼顾了特征重要性和可迁移性,还减少了“负迁移”,提高了模型泛化能力;然后,采用结合通道注意力机制的卷积神经网络SeNet构建基于深度迁移学习的质量预测模型。仿真结果表明,随着目标域样本的增加,所提方法的预测准确性明显优于广泛采用的支持向量机建模方法。同时,所提可迁移特征筛选方法显著提高了深度迁移学习的质量预测效果,为复杂的小批量生产过程质量保证提供了新方法。

农业生产性服务业与新型农业经营主体耦合协调关系的实证研究--以吉林省为例

在乡村振兴和农村产业融合大背景下,正确认识和处理农业生产性服务业和新型农业经营主体之间的协调发展关系具有重要意义。为明确农业生产性服务业与新型农业经营主体两系统的耦合协调关系,基于吉林省2010-2022年农业生产性服务业与新型农业经营主体的时间序列数据,运用协整分析、Granger因果检验和耦合协调模型分析农业生产性服务业与新型农业经营主体间的互动关系和动态耦合发展趋势。结果表明:吉林省农业生产性服务业与新型农业经营主体发展水平持续攀升,分别由0.284 9提升至0.837 9、0.100 1提升至0.994 1;农业生产性服务业与新型农业经营主体两系统存在着显著正相关的长期均衡关系,且互为格兰杰原因,即农业生产性服务业是有效促进新型农业主体发展的重要原因,新型农业经营主体是推动推动农业生产性服务业提升的重要动力;2010-2022年,吉林省农业生产性服务业与新型农业经营主体之间的耦合协调关系上升趋势明显,先后经历拮抗阶段(0.410 9)、勉强协调(0.517 9~0.572 0)、初级协调(0.637 4)、中级协调(0.735 8~0.773 8)、高度协调(0.843 1~0.870 9~0.916 6~0.914 7~0.914 8~0.915 3~0.915 7)的变化过程,目前两系统处于关系稳定的高水平耦合阶段。未来应通过促进农业生产性服务业与新型农业经营主体协调发展推进现代农业进程。

不确定条件下作战筹划态势研判风险预测方法

针对战场态势不确定、复杂繁多导致研判过程风险预测准确性差的问题,提出构建态势-风险模型并构造风险可能性分布以实现对风险的精准预测。首先,从敌情、我情以及战场环境态势研判内容入手建立风险预测指标体系;其次,剖析态势信息对风险的作用方式,构建指数、Z型、正切等风险模型并计算风险取值区间;然后,以可能性理论中模糊函数构造法为基础,以权重差和不确定性测度为依托,在风险取值区间内构造风险可能性分布;最后,采用某防御任务实例,结合专家系统及云模型方法进行实验,验证了所提方法的可行性和合理性。结果表明,在风险预测时所提方法能够充分考虑态势信息的不确定性,为后续筹划决策制定提供了重要依据。

裂缝性低渗油藏压裂水平井井网渗流数学模型

基于分形理论表征天然裂缝和压裂裂缝网络的复杂裂缝形态,针对裂缝性低渗透储层的非线性渗流特征和储层压敏特性,建立了裂缝性低渗透油藏压裂水平井-直井井组的非线性渗流模型。根据流体在混合井网中的不同流动形态,划分为3个区域,推导出了混合井网三区耦合产能公式,分析了分形维数、压敏效应、裂缝长度等参数对井网产能的影响。

热轧极致能效解决方案

钢铁行业CO_(2)减排已成为热点问题,各钢铁企业探索在保证产量、产品质量的前提下降低产品能耗。基于热轧机理模型,以预测控制、工艺参数优化为主要技术路径,提出了热轧工序节能的系统解决方案,从基础设施、机组预测、产线优化、基地平衡等4个维度进行阐述,并通过工程实践验证。该方案具有普适性,可推广到其他间歇式生产工序。

基于TFER及退化趋势相似性分析的轴承剩余使用寿命预测

为了解决传统退化指标对轴承退化起始点的敏感性差,以及退化指标趋势偏移导致无法准确预测风力机轴承剩余使用寿命(RUL)的问题,提出了一种基于Teager能量算子-故障能量比(TFER)与退化趋势(DT)相似性检测相结合的双指数轴承RUL预测方法。首先,通过计算原始信号的TFER值,根据4σ原则确定轴承退化起始点,提取了TFER值趋势特征作为退化指标;然后,采用历史TFER值拟合双指数退化模型,通过分析最新TFER值与拟合曲线的相似性,选取了最佳DT段;最后,通过外推相似性最佳的DT段至失效阈值,进行了风力机轴承RUL预测。实验结果表明:该预测方法对退化起始时间点的检测精度达到98%,与原始指数模型、长短期记忆神经网络(LSTM)以及支持向量回归(SVR)相比,该方法在轴承RUL预测精度上分别提高了10.04%、6.29%、5.22%。该方法不仅提升了风力机轴承的预测性维护精度,还对降低运营成本和提高维护效率提供了有力支撑。

自动化铸造生产线工艺特点分析

为推进机器人在铸造行业应用,深入分析了自动化铸造生产线各工艺工序的特点。介绍了自动化铸造生产线的整体工艺流程,详细讨论了砂芯浸涂和铸件打磨两个最为重要的工艺工序及其主要工艺参数。最后,对生产工序中出现频次最高的搬运操作进行了重点分析。

基于用能行为的广州城镇住宅运行碳排放预测模型研究

城镇住宅建筑碳排放量巨大,随着人民生活水平提高,用能行为对于运行碳排放的影响程度不容小觑。为提高碳排放预测的准确性,在考虑居民空调用能行为条件下,以广州地区城镇住宅建筑为研究对象,基于R语言参数化建模和EnergyPlus软件模拟结果,通过敏感性分析得到影响住宅全年冷负荷的关键因素,并建立不同建筑型式在不同用能行为下的运行碳排放预测模型。结果表明:当其他变量相同,居民用能行为由奢侈型分别转为适度型和节约型时,3种建筑型式住宅运行碳排放强度均分别降低15%和50%以上。

轨道交通车辆轮轴固死故障预测模型研究

[目的]由于轮轴组装不当、检修时操作过激使轮轴出现凹凸面或作业时电腐蚀造成擦伤缺损等情况,均会加剧轨道交通车辆轮轴热轴、轮轴固死等异常故障的发生。如不及时处理,可能造成严重的行车事故。因此,有必要研究轨道交通车辆轮轴固死故障预测方法及预防措施。[方法]以南京地铁S7号线列车出现轮轴固死故障为例,通过对列车监测数据的分析和挖掘,探究其潜在的相关性和周期性,为列车故障诊断提供依据;基于牵引模式,结合实时牵引电流、各架轴速、参考速度等信号进行大数据分析,建立轮轴固死故障预测模型;对轴承卡滞、轴内阻力增大及轴系老化导致性能下降等原因引起的轮轴固死现象进行判断,辨识其健康状态,实现对轮轴内部可能出现的一些老化、卡滞现象进行故障预测。[结果及结论]建立了轨道交通车辆轮轴固死故障预测模型;对列车实际运行数据进行分析,验证了该预测模型的可行性。但由于轮轴固死的样本数据很少,后续还需采用历史值结合新增值的方法进行在线学习,来提高模型预测的准确性。

数据报告:2023—2024年中国茧丝绸行业经济运行分析及展望

文章公布并分析了2023年中国茧丝绸行业的农业环节、工业环节、贸易环节的经济运行情况,并对2024年茧丝绸行业发展趋势进行展望。2023年,虽然全国桑园面积同比减少,但全国蚕种产量同比增长。受丝价上涨、蚕茧生产成本增加等影响,全国桑蚕茧收购均价也同比增长。丝绸行业规模以上企业主要产品产量同比有所下滑,但540家规模以上丝绸企业营业收入同比增长,2023年初以来,丝绸行业经济运行呈现持续恢复增长态势,企业营业收入和利润增速实现双增长。2023年,丝绸内销市场明显回暖,但真丝绸商品出口整体下滑。预计2024年国内蚕桑生产总体形势有望维持基本稳定格局,蚕茧产量能否实现较大幅度增长仍然存在较大变数,国内丝绸工业生产有望逐步走出低谷,生丝等主要产品产量降幅收窄,行业经济效益增速逐步趋缓,国内丝绸市场大概率延续温和回暖势头,但企业线上线下销售的压力和挑战仍然较大。考虑到目前国际消费市场形势仍不明朗,2024年国内丝绸商品出口能否实现止跌企稳,迎来阶段性拐点仍有待观察。

车务段安全生产指挥中心管理系统设计

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