外文翻譯--基于多數(shù)據(jù)融合傳感器的分布式溫度控制和溫度測量系統(tǒng)

1、<p>　　基于多數(shù)據(jù)融合傳感器的分布式溫度控制和溫度測量系統(tǒng)</p><p><b>　　摘要：</b></p><p>　　在過去的幾十年，溫度控制系統(tǒng)已經(jīng)被廣泛的應(yīng)用。對于溫度控制提出了一種基于多傳感器數(shù)據(jù)融合和CAN總線控制的一般結(jié)構(gòu)。一種新方法是基于多傳感器數(shù)據(jù)融合估計算法參數(shù)分布式溫控系統(tǒng)。該系統(tǒng)的重要特點是其共性，其適用于很多具體領(lǐng)域的大型的溫

2、度控制。實驗結(jié)果表明該系統(tǒng)具有較高的準(zhǔn)確性、可靠性，良好的實時性和廣泛的應(yīng)用前景。</p><p><b>　　關(guān)鍵詞：</b></p><p>　　分布式控制系統(tǒng)；CAN總線控制；智能CAN節(jié)點；多數(shù)據(jù)融合傳感器。</p><p><b>　　1介紹</b></p><p>　　分布式溫度控制系統(tǒng)

3、已經(jīng)被廣泛的應(yīng)用在我們?nèi)粘Ｉ詈蜕a(chǎn),包括智能建筑、溫室、恒溫車間、大中型糧倉、倉庫等。這種控制保證環(huán)境溫度能被保持在兩個預(yù)先設(shè)定的溫度間。在傳統(tǒng)的溫度測量系統(tǒng)中，我們用一個基于溫度傳感器的單片機(jī)系統(tǒng)建立一個RS-485局域網(wǎng)控制器網(wǎng)絡(luò)。借助網(wǎng)絡(luò),我們能實行集中監(jiān)控和控制.然而,當(dāng)監(jiān)測區(qū)域分布更廣泛和傳輸距離更遠(yuǎn),RS-485總線控制系統(tǒng)的劣勢更加突出。在這種情況下，傳輸和響應(yīng)速度變得更低，抗干擾能力更差。因此，我們應(yīng)當(dāng)尋找新的通信的方

4、法來解決用RS-485總線控制系統(tǒng)而產(chǎn)生的問題。在所有的通訊方式中，適用于工業(yè)控制系統(tǒng)的總線控制技術(shù)，我們可以突破傳統(tǒng)點對點通信方式的限制、建立一個真正的分布式控制與集中管理系統(tǒng)，CAN總線控制比RS-485總線控制系統(tǒng)更有優(yōu)勢。比如更好的糾錯能力、改善實時的能力，低成本等。目前，它正被廣泛的應(yīng)用于實現(xiàn)分布式測量和范圍控制。</p><p>　　隨著傳感器技術(shù)的發(fā)展，越來越多的系統(tǒng)開始采用多傳感器數(shù)據(jù)融合技術(shù)來提

5、高他們的實現(xiàn)效果。多傳感器數(shù)據(jù)融合是一種范式對多種來源整合數(shù)據(jù),以綜合成新的信息，比其他部分的總和更加強(qiáng)大。無論在當(dāng)代和未來，系統(tǒng)的低成本，節(jié)省資源都是傳感器中的一項重要指標(biāo)。</p><p>　　2分布式架構(gòu)的溫度控制系統(tǒng)</p><p>　　分布式架構(gòu)溫度控制系統(tǒng)如圖中所示的圖1?？梢钥闯?，這系統(tǒng)由兩個模塊——兩個智能CAN節(jié)點和一個主要的控制器組成。每個模塊部分執(zhí)行進(jìn)入分布式架構(gòu)。下

6、面的是簡短的描述下各模塊。</p><p><b>　　3.1主要控制器</b></p><p>　　作為系統(tǒng)的主要控制器，這主pc能和智能CAN節(jié)點通信。它致力于監(jiān)督和控制整個系統(tǒng)，系統(tǒng)配置、顯示運行狀況、參數(shù)初始化和協(xié)調(diào)各部分間的關(guān)系。更重要的是，我們能打印或儲存系統(tǒng)的歷史溫度的數(shù)據(jù)，這對分析系統(tǒng)性能是非常有用的。</p><p>　　3.

7、2智能CAN節(jié)點</p><p>　　每一個溫度控制系統(tǒng)的智能CAN節(jié)點有五個部分：MCU—一個單片機(jī)，A/D轉(zhuǎn)換單元，溫度監(jiān)測單元—傳感器群，數(shù)字顯示器，激發(fā)器—一個冷卻單元和供暖單元。接下來介紹智能CAN節(jié)點的工作原理。</p><p>　　在實際操作中，我們劃分控制的目標(biāo)進(jìn)入一些單元，儲存智能CAN節(jié)點在一些典型的單元。在每個節(jié)點，單片機(jī)借助A / D轉(zhuǎn)換單位從溫度測量傳感器收集溫度

8、數(shù)據(jù)。同時，它執(zhí)行基本的數(shù)據(jù)融合運算獲得運算的結(jié)果,更接近實際。數(shù)字顯示器及時顯示融合節(jié)點的結(jié)果，所以我們能及時了解在每個控制單元所處的環(huán)境溫度。</p><p>　　通過比較融合值用主控制器構(gòu)建一個，這樣智能CAN節(jié)點可以通過相應(yīng)的加熱或冷卻裝置實現(xiàn)反饋控制各單元。如果在特別的智能CAN節(jié)點融合結(jié)果大于設(shè)定值，冷卻單位將開始工作。相反,如果在節(jié)點融合的結(jié)果低于設(shè)定值加熱單位將開始工作。用這種方法,我們不僅能監(jiān)控

9、環(huán)境溫度,還能做相應(yīng)的觸發(fā)器來實現(xiàn)溫度的自動調(diào)節(jié)。與此同時，每個CAN節(jié)點發(fā)送數(shù)據(jù)幀到CAN總線，CAN總線將告知在著單元中的主控制器這溫度值，那么這控制器能便利的作出是否修改這參數(shù)的決定。自從這CAN節(jié)點有調(diào)節(jié)溫度的單元在，整個房間的溫度將保持均勻。更重要的是,我們也可以通過在主pc上修改溫度的設(shè)定值來控制這智能節(jié)點。</p><p>　　一般來說,處理器不擅長即時的復(fù)雜的數(shù)據(jù)處理和數(shù)據(jù)融合,所以如何選擇合適的

10、數(shù)據(jù)融合算法對系統(tǒng)變得至關(guān)重要。后一節(jié)中,我們將介紹適合于智能CAN節(jié)點的數(shù)據(jù)融合方法。</p><p>　　4.多傳感器數(shù)據(jù)融合</p><p>　　旨在利用數(shù)據(jù)融合在分布式溫度控制系統(tǒng)中來消除不確定性,獲得更精確、可靠是比從限定的傳感器的測量數(shù)據(jù)的算數(shù)平均值更重要。當(dāng)一些傳感器的溫度傳感器變?yōu)闊o效的，這智能CAN節(jié)點還可以通過熔斷這些信息而從有用的傳感器獲得精確溫度。</p>

11、;<p>　　4.1實測數(shù)據(jù)的一致性核實</p><p>　　在我們設(shè)計的分布式溫度控制系統(tǒng)的溫度測量的過程中,突發(fā)性干擾或設(shè)備故障的影響不可避免的產(chǎn)生測量誤差。所以在數(shù)據(jù)融合前我們應(yīng)該消除錯誤的誤差。</p><p>　　我們可以利用系統(tǒng)中配備的少量傳感器用散點圖發(fā)消除這個測量誤差。用參數(shù)來代表數(shù)據(jù)分布結(jié)構(gòu)包括中值——TM,上四位數(shù)—— Fv,下四位數(shù)——FL和分散四位數(shù)—

12、—dF.</p><p>　　人們認(rèn)為每個傳感器在溫度控制系統(tǒng)的溫度測量所得獨立。在系統(tǒng)中，有八個傳感器在各智能CAN節(jié)點的溫度傳感器群。所以我們在每個CAN節(jié)點同一時刻能獲得8個溫度值。我們安排收集到的溫度數(shù)據(jù)序列由小到大:</p><p>　　T1, T2, …, T8 </p><p>　　在序列中，T1是最低位而T8是最高位。我們定義TM為：</p&g

13、t;<p>　　上四位數(shù)——Fv是區(qū)間[TM, T8]的中值，低四位數(shù)—— Fl是區(qū)間[T1, TM]的中值，這四位數(shù)的離散是：。</p><p>　　該公式,一個是常數(shù),取決于系統(tǒng)測量誤差, 通常值是0.5,1.0,2.0等等。在數(shù)列中其余的測量值都被看作是于有效值一致的。在智能CAN節(jié)點的單片機(jī)智將把一致的測量值融合。</p><p>　　5. 溫度測量的數(shù)據(jù)融合的舉例&

14、lt;/p><p>　　分布式溫度控制系統(tǒng)運用于一間溫室, 我們從8個溫度傳感器獲得一組8個溫度值如下</p><p>　　八個溫度測量值的結(jié)果</p><p>　　把在這溫室中的八個溫度的平均值和真實的溫度值做比較，我們可以知道測量誤差是+ 0.5℃。之后在介紹這個方法前我們消除從這第五個傳感器的測量誤差，我們能得到的剩余的七個數(shù)據(jù)的平均值(7)T = 29.6℃,

15、測量誤差是-0.4℃.這剩下的七個傳感器被分成兩個傳感器組，S1, S3, S7 是第一組，S2, S4, S6, S8 是第二組。兩組測量數(shù)據(jù)的算術(shù)平均和標(biāo)準(zhǔn)偏差分別如下：</p><p>　　根據(jù)公式(13), 我們可以用七個測量溫度確定溫度融合值。</p><p>　　融合溫度的結(jié)果的誤差是-0.3℃。</p><p>　　很明顯,數(shù)據(jù)融合測量結(jié)果比算術(shù)的平均

16、值更接近于實際值。在實際操作中,測量溫度可能是很分散的變得更大的監(jiān)測區(qū)域,數(shù)據(jù)融合將更加明顯提高了測量精度。</p><p><b>　　6.總結(jié)</b></p><p>　　這基于多數(shù)據(jù)融合傳感器的分布式溫度控制系統(tǒng)是通過CAN總線構(gòu)建。它充分利用了FDCS即時總線控制系統(tǒng)的特點。數(shù)據(jù)采集，數(shù)據(jù)融合，系統(tǒng)控制用智能CAN節(jié)點得到實現(xiàn)，而系統(tǒng)管理通過主控制器（host

17、 PC）被實現(xiàn)。通過使用CAN總線與數(shù)據(jù)融合技術(shù)系統(tǒng)的可靠性和實時的能力被大大提高了。我們確定它在將來會得到廣泛的應(yīng)用。</p><p>　　DISTRIBUTED TEMPERATURE CONTROL TEMPERATURE MEASURING SYSTEM BASED ON MULTI-SENSOR DATA FUSION</p><p>　　Abstract: </p>

18、<p>　　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

19、 bus. A new 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 field

20、s of large scale temperature control. Experiment shows that this system p</p><p>　　Keywords: </p><p>　　Distributed control system; CAN bus; intelligent CAN node; multi-sensor data fusion.</p&

21、gt;<p>　　1. Introduction </p><p>　　Distributed temperature control system has been widely used in our daily life and production, including intelligent building, greenhouse, constant temperature worksh

22、op, large and medium granary, depot, and so on[1]. This kind of system should ensure that the environment temperature can be kept between two predefined limits. In the conventional temperature measurement systems we buil

23、d a network through RS-485 Bus using a single-chip metering system based on temperature sensors. With the aid of th</p><p>　　During all the communication manners, the industrial control-oriented field bus te

24、chnology 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

25、supporting distributed real-time control, CAN bus has much more merits than RS-485 Bus, such as better error correction ability, better real-time ability, lower cost and so on. Presently, it has bee</p><p>　

26、　With the development of sensory technology, more and more systems begin to adopt multi-sensor data fusion technology to improve their performances. Multi-sensor data fusion is a kind of paradigm for integrating the data

27、 from multiple sources to synthesize the new information so that the whole is greater than the sum of its parts [3][4][5]. And it is a critical task both in the contemporary and future systems which have distributed netw

28、orks of low-cost, resource-constrained sensors</p><p>　　2. Distributed architecture of the temperature control system </p><p>　　The distributed architecture of the temperature control system is

29、depicted in the Figure 1. As can be seen, the system consists of two modules—several intelligent CAN nodes and a main controller. They are interconnected with each other through CAN bus. Each module performs its part int

30、o the distributed architecture. The following is a brief description of each module in the architecture. </p><p>　　3．1main controller</p><p>　　As the system’s main controller, the host PC can co

31、mmunicate 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 be

32、tween each part. What’s more, we can print or store the system’s history temperature data, which is very useful for the analysis of the system performance</p><p>　　3.2. Intelligent CAN node </p><p

33、>　　Each intelligent CAN node of the temperature control system includes five units: MCU—a single chip, A/D conversion unit, temperature monitoring unit—sensor group, digital display unit and actuators—a cooling unit a

34、nd a heating unit. The operating principle of the intelligent CAN node is described as follows. </p><p>　　In the practical application, we divide the region of the control objective into many cells, and lay

35、the intelligent CAN nodes in some of the typical cells. In each node, MCU collects temperature data from the temperature measurement sensor groups with the aid of the A/D conversion unit. Simultaneously, it performs basi

36、c 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, so we can unders</p><p>　　By comparing the f

37、usion 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 devices. If the fusion resu

38、lt 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.

39、By this means we can not only monitor the </p><p>　　Generally, the 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 fu

40、sion algorithm for the system. In the posterior section, we will introduce a data fusion method which is suitable for the intelligent CAN nodes。</p><p>　　4. Multi-sensor data fusion </p><p>　　Th

41、e 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. Furtherm

42、ore, when some of the sensors become invalid in the temperature sensor groups, the intelligent CAN node can still obtain the accurate temperature value by fusing the information from the other valid sensors. </p>

43、<p>　　4.1. Consistency verification of the measured data </p><p>　　During the process of temperature measurement in our designed distributed temperature control system, measurement error comes into bein

44、g inevitably because of the influence of the paroxysmal disturb or the equipment fault. So we should eliminate the careless mistake before data fusion. </p><p>　　We can eliminate the measurement errors by us

45、ing scatter diagram method in the system equipped with little amount of sensors. Parameters to represent the data distribution structure include median—TM, upper quartile number—Fv, lower quartile number—FL and quartile

46、dispersion—dF. </p><p>　　It is supposed that each sensor in the temperature control system proceeds temperature measurement independently. In the system, there are eight sensors in each temperature sensor gr

47、oup 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: </p><p>　　T1, T2, …,

48、 T8 </p><p>　　In the sequence, T1 is the limit inferior and T8 is the limit superior. </p><p>　　We define the median—TM as: </p><p><b>　　(1) <

49、;/b></p><p>　　The upper quartile—Fv is the median of the interval [TM, T8].The lower quartile number—FL is the median of the interval [T1, TM].The dispersion of the quartile is: </p><p><b

50、>　?。?）</b></p><p>　　We suppose that the data is an aberration one if the distance from the median is greater than adF, that is, the estimation interval of invalid data is: </p><p><b

51、>　　(3)</b></p><p>　　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. </p><p>　　The rest values i

52、n the measurement column are considered as to be the valid ones with consistency. And the Single-Chip in the intelligent CAN node will fuse the consistent measurement value to obtain a fusion result</p><p>　

53、　5. Temperature measurement data fusion experiment </p><p>　　By applying the distributed temperature control system to a greenhouse, we obtain an array of eight temperature values from eight sensors as follo

54、ws</p><p>　　The mean value of the eight measurement temperature result is</p><p>　　Comparing the mean value (8)T with the true temperature value in the cell of the greenhouse, we can know that t

55、he measurement error is +0.5℃. After we eliminate the careless error from the fifth sensor using the method introduced before, we can obtain the mean value of the rest seven data (7)T=29.6℃, the measurement error is -0.4

56、℃. </p><p>　　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

57、measured data and the standard deviation are as follows respectively:</p><p>　　According to formula (13), we can educe the temperature fusion value with the seven measured temperature value. </p><

58、p>　　The error of the fusion temperature result is -0.3℃. </p><p>　　It is obvious that the measurement result from data fusion is more close to the true value than that from arithmetical mean. In the pract

59、ical 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.</p><p>　　6. Conclusions &l

60、t;/p><p>　　The distributed temperature control system based on multi-sensor data fusion is constructed through CAN bus. It takes full advantage of the characteristics of field bus control system---FDCS. Data ac

61、quisition, data fusion and system controlling is carried out in the intelligent CAN node, and system management is implemented in the main controller (host PC). By using CAN bus and data fusion technology the reliability

62、 and real-time ability of the system is greatly improved. We are sure that it wil</p><p>　　References </p><p>　　[1] Waltz E. Liinas J, Multi-sensor Data Fusion, Artech House, New York, 1990. <

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