柴油机调速系统动态仿真与最佳设计分析

采用状态空间法对柴油机调速系统的动态过程进行了建模与仿真，并分析了调速器最佳设计的主要参数变化规律，为设计调速器提供有益的依据。     

A new thermographic NDT for condition monitoring of electrical components using ANN with confidence level analysis

Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg–Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.     

水杨酸甲酯的合成工艺研究

通过水杨酸和甲醇为原料,分别在浓硫酸和硫酸氢钾催化作用下合成了水杨酸甲酯。考察了酸醇摩尔比、反应时间及催化剂用量对酯收率的影响。结果表明,使用硫酸氢钾做催化剂,n(水杨酸):n(甲醇)摩尔比=1:5,反应5 h,所获得的水杨酸甲酯的收率为最好,平均收率可以到达70%。     

邻二氮菲分光光度法测定铁条件选择实验的改进

普通教材中邻二氮菲分先光度法测定铁条件选择实验都是单因素条件实验,不够全面,实验设计中也存在一些问题.因为多因素条件选择实验准确率高,在pH值、显色剂用量年口显色时间三个条件同时改变的情况下,改进实验观察到pH<3时显色反应速度慢且不完全,而pH>9时溶液会逐渐褪色等等实验现象,通过对实验现象作出合理的科学解释再设计验证实验加以验证的方法,获得了良好的教学效果.     

Optimal balancing of flexible rotors by minimizing the condition number of influence coefficients

The Hermitian matrix of influence coefficients is one indicator of a balancing experiment’s overall effectiveness. The condition number of this matrix is determined by the sensor locations and balancing planes selected for the balancing procedure. In this research, we use finite element analysis to simulate the balancing of flexible rotor-bearing systems under various arrangements of sensors and planes. The condition number turns out to be inversely related to the accuracy of the balancing and directly related to the sum of correction masses; in other words, the accuracy and efficiency of rotor balancing can be improved by selecting sensor locations and balancing planes which reduce the condition number. This result is validated by experimental measurements of a physical rotor system.     

Effect of reaction conditions on the catalytic performance of Co<Subscript>9</Subscript>Fe<Subscript>3</Subscript>Bi<Subscript>1</Subscript>Mo<Subscript>12</Subscript>O<Subscript>51</Subscript> in the oxidative dehydrogenation of n-butene to 1,3-butadiene

Oxidative dehydrogenation of n-butene to 1,3-butadiene over Co₉Fe₃Bi₁Mo₁₂O₅₁ catalyst was conducted in a continuous flow fixed-bed reactor. The effect of reaction conditions (steam/n-butene ratio, reaction temperature, and space velocity) on the catalytic performance of Co₉Fe₃Bi₁Mo₁₂O₅₁ was investigated. Steam played an important role in decreasing contact time, suppressing total oxidation of n-butene, and removing coke during the reaction. Yield for 1,3-butadiene showed a volcano-shaped curve with respect to steam/n-butene ratio. The compensation between thermodynamic effect and kinetic effect led to a volcano-shaped curve of 1,3-butadiene yield with respect to reaction temperature. The Co₉Fe₃Bi₁Mo₁₂O₅₁ catalyst showed the best catalytic performance at a certain value of space velocity. The optimum steam/n-butene ratio, reaction temperature, and gas hourly space velocity were found to be 15, 420 °C, and 675 h⁻¹, respectively.     

可拓工程方法在火电厂煤粉着火及时性评价中的应用

考虑到煤粉能否及时着火对于电站锅炉安全经济运行的重要程度,提出基于物元概念和可拓集合理论的可拓物元评价方法.在建立评价等级指标和评价模型的基础上,通过计算与煤粉燃烧及时性有关的状态参数和各种评价等级之间的可拓关联度,由关联度的大小判断锅炉煤粉的着火状态是否及时.最后通过实例表明,此方法用于评价锅炉煤粉着火状态的有效性、准确性及工程实用性.     

一种军用工程车辆车载状态监测系统设计

以军用工程车辆中挖掘机为研究对象,根据其工作原理、结构、使用、保养、维修情况,进行监测系统的总体设计,并以ARM微控制器为核心,应用嵌入式技术完成了信号采集及处理、液晶显示、数据存储等模块硬件设计,在KeilC开发环境下进行了系统软件开发,具有实时性好、可靠性高、监测精度高、系统扩展方便等特点,提高了军用工程车辆的在线故障检测与现场快速维修保障能力。     

SIMS(DAIRY): a modelling framework to identify sustainable dairy farms in the UK. Framework description and test for organic systems and N fertiliser optimisation

Multiple demands are placed on farming systems today. Society, national legislation and market forces seek what could be seen as conflicting outcomes from our agricultural systems, e.g. food quality, affordable prices, a healthy environmental, consideration of animal welfare, biodiversity etc., Many of these demands, or desirable outcomes, are interrelated, so reaching one goal may often compromise another and, importantly, pose a risk to the economic viability of the farm.SIMS_DAIRY, a farm-scale model, was used to explore this complexity for dairy farm systems. SIMS_DAIRY integrates existing approaches to simulate the effect of interactions between farm management, climate and soil characteristics on losses of nitrogen, phosphorus and carbon. The effects on farm profitability and attributes of biodiversity, milk quality, soil quality and animal welfare are also included. SIMS_DAIRY can also be used to optimise fertiliser N.In this paper we discuss some limitations and strengths of using SIMS_DAIRY compared to other modelling approaches and propose some potential improvements. Using the model we evaluated the sustainability of organic dairy systems compared with conventional dairy farms under non-optimised and optimised fertiliser N use. Model outputs showed for example, that organic dairy systems based on grass-clover swards and maize silage resulted in much smaller total GHG emissions per l of milk and slightly smaller losses of NO₃ leaching and NO_x emissions per l of milk compared with the grassland/maize-based conventional systems. These differences were essentially because the conventional systems rely on indirect energy use for ‘fixing’ N compared with biological N fixation for the organic systems. SIMS_DAIRY runs also showed some other potential benefits from the organic systems compared with conventional systems in terms of financial performance and soil quality and biodiversity scores. Optimisation of fertiliser N timings and rates showed a considerable scope to reduce the (GHG emissions per l milk too).     

Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive normal behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three normal behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies.The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abnormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.