外文翻译 基于多数据融合传感器的分布式温度控制系统

1、DISTRIBUTED TEMPERATURE CONTROL SYSTEM BASED ON MULTI-SENSOR DATA FUSION Abstract: Temperature control system has been widely used over the past decades. In this paper, a general architecture of distributed temperature control system is put forward based on multi-sensor data fusion and CAN bus. A ne

2、w method of multi-sensor data fusion based on parameter estimation is proposed for the distributed temperature control system. The major feature of the system is its generality, which is suitable for many fields of large scale temperature control. Experiment shows that this system possesses higher a

3、ccuracy, reliability, good realtime characteristic and wide application prospectKeywords: Distributed control system; CAN bus; intelligent CAN node; multi-sensor data fusion.1. Introduction Distributed temperature control system has been widely used in our daily life and production, including intell

4、igent building, greenhouse, constant temperature workshop, large and medium granary, depot, and so on1. This kind of system should ensure that the environment temperature can be kept between two predefined limits. In the conventional temperature measurement systems we build a network through RS-485

5、Bus using a single-chip metering system based on temperature sensors. With the aid of the network, we can carry out centralized monitoring and controlling. However, when the monitoring area is much more widespread and transmission distance becomes farther, the disadvantages of RS-485 Bus become more

6、 obvious. In this situation, the transmission and response speed becomes lower, the anti-interference ability becomes worse. Therefore, we should seek out a new communication method to solve the problems produced by RS-485 Bus.During all the communication manners, the industrial control-oriented fie

7、ld bus technology can ensure that we can break through the limitation of traditional point to point communication mode and build up a real distributed control and centralized management system. As a serial communication protocol supporting distributed real-time control, CAN bus has much more merits

8、than RS-485 Bus, such as better error correction ability, better real-time ability, lower cost and so on. Presently, it has been extensively used in the implementation of distributed measurement and control domains. With the development of sensory technology, more and more systems begin to adopt mul

9、ti-sensor data fusion technology to improve their performances. Multi-sensor data fusion is a kind of paradigm for integrating the data from multiple sources to synthesize the new information so that the whole is greater than the sum of its parts . And it is a critical task both in the contemporary

10、and future systems which have distributed networks of low-cost, resource-constrained sensors2. Distributed architecture of the temperature control system The distributed architecture of the temperature control system is depicted in the Figure 1. As can be seen, the system consists of two modulesseve

11、ral intelligent CAN nodes and a main controller. They are interconnected with each other through CAN bus. Each module performs its part into the distributed architecture. The following is a brief description of each module in the architecture. 31main controllerAs the systems main controller, the hos

12、t PC can communicate with the intelligent CAN nodes. It is devoted to supervise and control the whole system, such as system configuration, displaying running condition, parameter initialization and harmonizing the relationships between each part. Whats more, we can print or store the systems histor

13、y temperature data, which is very useful for the analysis of the system performance3.2. Intelligent CAN node Each intelligent CAN node of the temperature control system includes five units: MCUa single chip, A/D conversion unit, temperature monitoring unitsensor group, digital display unit and actua

14、torsa cooling unit and a heating unit. The operating principle of the intelligent CAN node is described as follows. In the practical application, we divide the region of the control objective into many cells, and lay the intelligent CAN nodes in some of the typical cells. In each node, MCU collects

15、temperature data from the temperature measurement sensor groups with the aid of the A/D conversion unit. Simultaneously, it performs basic data fusion algorithms to obtain a fusion value which is more close to the real one. And the digital display unit displays the fusing result of the node timely,

16、so we can understand the environment temperature in every control cell separately. By comparing the fusion value with the set one by the main controller, the intelligent CAN node can implement the degenerative feedback control of each cell through enabling the corresponding heating or cooling device

17、s. If the fusion result is bigger than the set value in the special intelligent CAN node, the cooling unit will begin to work. On the contrary, if the fusion result is less than the set value in the node the heating unit will begin to work. By this means we can not only monitor the environment tempe

18、rature, but also can make the corresponding actuator work so as to regulate the temperature automatically. At the same time every CAN node is able to send data frame to the CAN bus which will notify the main controller the temperature value in the cell so that controller can conveniently make decisi

19、ons to modify the parameter or not. Since the CAN nodes can regulate the temperature of the cell where they are, the temperature in the whole room will be kept homogeneous. Whats more, we can also control the intelligent node by modifying the temperatures setting value on the host PC.Generally, the

20、processors on the spot are not good at complex data processing and data fusing, so it becomes very critical how to choose a suitable data fusion algorithm for the system. In the posterior section, we will introduce a data fusion method which is suitable for the intelligent CAN nodes。4. Multi-sensor

21、data fusion The aim to use data fusion in the distributed temperature control system is to eliminate the uncertainty, gain a more precise and reliable value than the arithmetical mean of the measured data from finite sensors. Furthermore, when some of the sensors become invalid in the temperature se

22、nsor groups, the intelligent CAN node can still obtain the accurate temperature value by fusing the information from the other valid sensors. 4.1. Consistency verification of the measured data During the process of temperature measurement in our designed distributed temperature control system, measu

23、rement error comes into being inevitably because of the influence of the paroxysmal disturb or the equipment fault. So we should eliminate the careless mistake before data fusion. We can eliminate the measurement errors by using scatter diagram method in the system equipped with little amount of sen

24、sors. Parameters to represent the data distribution structure include medianTM, upper quartile numberFv, lower quartile numberFL and quartile dispersiondF. It is supposed that each sensor in the temperature control system proceeds temperature measurement independently. In the system, there are eight

25、 sensors in each temperature sensor group of the intelligent CAN node. So we can obtain eight temperature values in each CAN node at the same time. We arrange the collected temperature data in a sequence from small to large: T1, T2, , T8 In the sequence, T1 is the limit inferior and T8 is the limit

26、superior. We define the medianTM as: (1) The upper quartileFv is the median of the interval TM, T8.The lower quartile numberFL is the median of the interval T1, TM.The dispersion of the quartile is: 2We suppose that the data is an aberration one if the distance from the median is greater than adF, t

27、hat is, the estimation interval of invalid data is: (3) In the formula, a is a constant, which is dependent on the system measurement error, commonly its value is to be 0.5, 1.0, 2.0 and so on. The rest values in the measurement column are considered as to be the valid ones with consistency. And the

28、 Single-Chip in the intelligent CAN node will fuse the consistent measurement value to obtain a fusion result 5. Temperature measurement data fusion experiment By applying the distributed temperature control system to a greenhouse, we obtain an array of eight temperature values from eight sensors as

29、 followsThe mean value of the eight measurement temperature result isComparing the mean value (8)T with the true temperature value in the cell of the greenhouse, we can know that the measurement error is +. After we eliminate the careless error from the fifth sensor using the method introduced befor

30、e, we can obtain the mean value of the rest seven data (7)T=, the measurement error is . The seven rest consistent sensor can be divided into two groups with sensor S1, S3, S7 in the first group and sensor S2, S4, S6, S8 in the second one. The arithmetical mean of the two groups of measured data and

31、 the standard deviation are as follows respectively:According to formula (13), we can educe the temperature fusion value with the seven measured temperature value. The error of the fusion temperature result is . It is obvious that the measurement result from data fusion is more close to the true val

32、ue than that from arithmetical mean. In the practical application, the measured temperature value may be very dispersive as the monitoring area becomes bigger, data fusion will improve the measuring precision much more obviously.6. Conclusions The distributed temperature control system based on mult

33、i-sensor data fusion is constructed through CAN bus. It takes full advantage of the characteristics of field bus control system-FDCS. Data acquisition, data fusion and system controlling is carried out in the intelligent CAN node, and system management is implemented in the main controller (host PC)

34、. By using CAN bus and data fusion technology the reliability and real-time ability of the system is greatly improved. We are sure that it will be widely used in the future.References 1 Waltz E. Liinas J, Multi-sensor Data Fusion, Artech House, New York, 1990. 2 Philips Semiconductors, (1995b). “P82

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38、andell N R, Detection with distributed sensors, AES, Vol 17, pp.501-510, 1981 基于多数据融合传感器的分布式温度控制系统摘要： 在过去的几十年，温度控制系统已经被广泛的应用。对于温度控制提出了一种基于多传感器数据融合和CAN总线控制的一般结构。一种新方法是基于多传感器数据融合估计算法参数分布式温控系统。该系统的重要特点是其共性，其适用于很多具体领域的大型的温度控制。实验结果说明该系统具有较高的准确性、可靠性，良好的实时性和广泛的应用前景。关键词： 分布式控制系统；CAN总线控制；智能CAN节点；多数据融合传感器。1介绍

39、 分布式温度控制系统已经被广泛的应用在我们日常生活和生产,包括智能建筑、温室、恒温车间、大中型粮仓、仓库等。这种控制保证环境温度能被保持在两个预先设定的温度间。在传统的温度测量系统中，我们用一个基于温度传感器的单片机系统建立一个RS-485局域网控制器网络。借助网络,我们能实行集中监控和控制.然而,当监测区域分布更广泛和传输距离更远,RS-485总线控制系统的劣势更加突出。在这种情况下，传输和响应速度变得更低，抗干扰能力更差。因此，我们应当寻找新的通信的方法来解决用RS-485总线控制系统而产生的问题。在所有的通讯方式中，适用于工业控制系统的总线控制技术，我们可以突破传统点对点通信方式的限制、

40、建立一个真正的分布式控制与集中管理系统，CAN总线控制比RS-485总线控制系统更有优势。比方更好的纠错能力、改善实时的能力，低本钱等。目前，它正被广泛的应用于实现分布式测量和范围控制。 随着传感器技术的开展，越来越多的系统开始采用多传感器数据融合技术来提高他们的实现效果。多传感器数据融合是一种范式对多种来源整合数据,以综合成新的信息，比其他局部的总和更加强大。无论在当代和未来，系统的低本钱，节省资源都是传感器中的一项重要指标。2分布式架构的温度控制系统 分布式架构温度控制系统如图中所示的图1。可以看出，这系统由两个模块两个智能CAN节点和一个主要的控制器组成。每个模块局部执行进入分布式架构。

41、下面的是简短的描述下各模块。主要控制器 作为系统的主要控制器，这主pc能和智能CAN节点通信。它致力于监督和控制整个系统，系统配置、显示运行状况、参数初始化和协调各局部间的关系。更重要的是，我们能打印或储存系统的历史温度的数据，这对分析系统性能是非常有用的。智能CAN节点 每一个温度控制系统的智能CAN节点有五个局部：MCU一个单片机，A/D转换单元，温度监测单元传感器群，数字显示器，激发器一个冷却单元和供暖单元。接下来介绍智能CAN节点的工作原理。 在实际操作中，我们划分控制的目标进入一些单元，储存智能CAN节点在一些典型的单元。在每个节点，单片机借助A / D转换单位从温度测量传感器收集温度数据。同时，它执行根本的数据融合运算获得运算的结果,更接近实际。数字显示器及时显示融合节点的结果，所以我们能及时了解在每个控制单元所处的环境温度。 通过比拟融合值用主控制器构建一个，这样智能CAN节点可以通过相应的加热或冷却装置实现反应控制各单元。如