外文翻译--- 高精度温度测量装置的设计.docx

附录ADesign of a High Precision Temperature Measurement System1 IntroductionSensors are one of the most important elements used in many instrumentation circuits. They are used in many industrial applications and take a certain form of input (temperature, pressure, altitude, etc.) and convert it into readings that can be interpreted. Many types of sensors are nonlinear in nature from which a linear output is desired. There are many different sensors for temperature measurement and thermocouples are the most commonly used . They are preferred in industrial applications due to their low cost, wide operation range, fast response time and accurate when their peculiarities are understood. Thermocouples have also outputs nonlinearly related to temperature. Therefore, sensor modeling and linearization techniques are necessary. To solve the linearization problem of a sensor, there are generally two methods proposed. The first one requires nonlinear analog circuit andthe second uses numerical methods that are computed by microprocessor or computer . Analog circuits are frequently used for improving the linearity of the sensor characteristics, which implies additional analog hardware and typical problems associated to analog circuits such as temperature drift, gain and offset error. Using the second method, sensor nonlinearities can be compensated by means of arithmetic operations, if an accurate sensor model is available (direct computation of the polynomials), or use of a multidimensional look-up table. Direct computation of the polynomial method is more accurate but takes a long time for computation, while the look-up table method, though faster, is not very accurate . In recent years, application of ANNs has emerged as a promising area of research in the field of instrumentation and measurement . It provides a neurocomputing approach for solving complex problems especially in nonlinear system modeling which the network itself is a nonlinear system. This is extremely useful when the area of interest is absolutely nonlinear including the experimental data that is used for training. One of the most powerful uses of ANNs is in function approximation (curve fitting) . Interpolation based on ANN provides lower interpolation errors when compared with conventional numerical interpolation . In the work we present here, high precision temperature measurement system based on ANN approach is proposed. The calibrating data is obtained by Wavetek 9100 calibration unit that is necessary for the training and the testing phases of the ANN. The hardware and software parts of the system are integrated in a VI used for system operation and calibration. The ANN is matched to the calibrating data by providing a desired final error. The mean square error between calibration and the ANN modeled data is minimized in terms of the structure, number of layers, and number of neurons by the developed software.2 System HardwareA thermocouple generates a voltage proportional to the measurement junction temperature at mV levels while the cold junction temperature is constant. In order to make an accurate measurement the cold junction temperature must be known. Figure 1(a) shows the block diagram of the temperature measurement system designed via an ANN in the operation phase. It consists of a thermocouple (type E) exposed to a desired temperature, including signal conditioning circuit with 16-bit analog to digital converter (ADC) and Input/Output interface card interfacing with a computer. The designed signal conditioning circuit has a programmable gain instrumentation amplifier (PGA204BP) with the gain of 1, 10, 100 and 1000, a 16-bit ADC (AD976A), an AD595 monolithic thermocouple amplifier with cold junction compensation which is configured as a stand-alone Celsius thermometer and a 4 channel analog multiplexer (ADG529A) which select the thermocouple or output of Celsius thermometer. The AD976A is a high speed, low power 16-bit A/D converter that operates from a single 5V supply. This part contains a successive approximation, switched capacitor ADC, an internal 2.5V reference and a high speed parallel interface. Accuracy of the system depends directly on step size of ADC. With a 10V inputs, one LSB of AD976A is 305V. When AD595 is used as a Celsius thermometer, the thermocouple is omitted, and the differential inputs are shunted together to common. In this mode, AD595 generate a voltage with a scale factor of 10mV/C and its output is used for cold junction temperature data that the written software is used. Some important characteristics of the AD595 are: operation temperature range -55 to 125C; stability vs. temperature: 0.05C/C and sensitivity: 10mV/C. Output signal of PGA204BP is digitized by AD976A which its output is connected to the I/O interface card and transferred to a personal computer where data reduction and optimization are implemented.Fig. 1. Measurement system block diagram: (a) operational phase, (b) calibration phase To establish the ANNs weights and biases, during the calibrating phase (ANN training phase), Wavetek 9100 calibration unit, with the accuracy of 0.006%+4.16V in the range of 000.000mV to 320.000mV, is connected to the terminals of analog multiplexer to generate tabled thermocouple voltages as shown in Figure 1 (b). This voltage is used as the input of the ANN, and thermocouple temperature without cold junction compensation is the output of the ANN. In the operation phase (Figure 1(a), in order to make the cold junction compensation, data taken from Celsius thermometer output is used. The output value of ANN is shifted by the environment temperature that is obtained by Celsius thermometer. Then this value is displayed on the VI as the thermocouple temperature. The developed VI is used to acquire the data for ANN training phase and to show the calculated temperature in the operation phase. Figure 2 shows the front panel of the VI. The main features associated with this instrument are: display of the measured temperature and corresponding output voltage from conditioning circuit for collecting the data in the calibrating phase and actual temperature with cold junction compensation in the operation phase. The system is controlled by the software written in both operation and calibration phases.3 Artificial Neural NetworkANNs are based on the mechanism of the biologically inspired brain model. ANNs are feed-forward networks and universal approximators. They are trained and learned through experience not from programming. They are formed by interconnections of simple processing elements, or neurons with adjustable weights, which constitute the neural structure and are organized in layers. Each artificial neuron has weighted inputs, summation and activation functions and output. The behaviour of the overall ANN depends upon the operations mentioned on the artificial neurons, the learning rule and the architecture of the network. During the training (learning), the weights between the neurons are adjusted according to some criterion (The mean square error between the target output and the measured value for all the training set falls below a predetermined threshold) or the maximum allowable number of epochs is reached. Although the training is a time consuming process, it can be done beforehand, offline. The trained neural network is then tested using data was previously unseen during training.MLPs are the simplest and most commonly used neural network architectures . They consists of input, output and one or more hidden layers with a predefined number of neurons. The neurons in the input layer only act as buffers for distributing the input signals xi to neurons in the hidden layer. Each neuron j in the hidden layer sums up its input signals xi, after weighting them with the strengths of the respective connections wji from the input layer and computes its output yj as a function f of the sum, namely where f is one of the activation functions used in ANN architecture.Training a neural network consists of adjusting the network weights using different learning algorithms. A learning algorithm gives wji(t) in the weight of a connection between neurons i and j at time t. The weights are then updated according to the following formula: There are many available learning algorithms in the literature . The algorithms used to train ANNs in this study are LevenbergMarquardt (LM) , BroydenFletcherGoldfarbShanno (BFGS) , Bayesian Regularization (BR) , Conjugate gradient backpropagation with Fletcher-Reeves updates (CGF) , and Resilient back-propagation (RP) algorithms. Neural LinearizationIn this paper, the multilayered perceptron (MLP) neural network architecture is used as a neural linearizer. The proposed technique involves an ANN to evaluate the thermocouple temperature (ANN output) when thermocouple output voltage is given as input. Training the ANN with the use of mentioned learning algorithm to calculate the temperature involves presenting it with different sets of input values and corresponding measured values. Differences between the target output and the actual output of the ANN are evaluated by the learning algorithm to adapt the weights using equations (1) and (2).The experimental data taken from thermocouple data sheets are used in this investigation. These data sheets are prepared for a particular junction temperature (usually 0C). The ANN is trained with 80 thermocouple temperatures that is uniformly distributed between -200 and 1000C which is obtained in the calibration phase. However the performance of the final network with the training set is not an unbiased estimate of its performance on the universe of possible inputs, and an independent test set is required to evaluate the network performance after training. Therefore, the other data set of 20 thermocouple temperatures that is uniformly distributed between -200 and 1000C, is used in the test process. The input and output data tuples are normalized between -1.0 and 1.0 before training. After several trials with different learning algorithms and with different network configurations, it is found that the most suitable network configuration is 1 X 7 X 3 X 1 with the LM algorithm. This means that the number of neurons is 7 for the first hidden layer and 3 for the second hidden layer respectively. The input and output layers have the linear activation function and the hidden layers have the hyperbolic tangent sigmoid activation function. The number of epoch is 1000 for training. It is important to note that the criteria for too small and too big hidden layer neuron numbers depend on a large number of factors, like ANN type, training set characteristics and type of application. This topic is still under special attention of artificial intelligence researchers today.4 Results and ConclusionThe developed ANN models are trained and tested with the use of different learning algorithms called LM, BR, CGF, RP and BFGS to obtain better performance and faster convergence with simpler structure. Table 1 shows the errors from the complete learning algorithms used in the analysis for the same network configuration mentioned above. When the performances of the neural models are compared with each other, the best result for the training and the test are obtained from the model trained with the LM algorithm. The training and test errors (MSE, mean square error) of the network for the LM algorithm are 0.7x10-9 and 1.3x10-4 respectively. As it is clearly seen from Table 1, the next solution which is closer to LM is obtained from BR algorithm. Among neural models presented here, the worst results are obtained from the RP method for this particular application. It should be emphasized that the accuracy of learning algorithms in general depends on selecting appropriate learning parameters, network configurations and initializations.Figure 3 represents the percentage test error of the network trained with LM for type E thermocouple. As it is clearly seen from Figure 3, the maximum percentage error becomes lower than 0.3%. The average percentage error is greater than 0.1% for temperatures between -200 and 200C, the reason being that in this range the thermocouples are strongly nonlinear. However, it is obvious for best fit in the range - 200 to 200C that the number of training data set must be increased. The normalized error convergence curves in the learning algorithms used in the analysis for 1000 Fig. 4. Learning (convergence) characteristics of the ANN for different learning algorithms used in the analysis for 1000 epoch.epochs are graphically shown in Figure 4. For simulating the neural model, the ANN is trained, to minimize the MSE. As the learning proceeds, the mean square error progressively decreases and finally attains a steady state minimum value as shown in Figure 4. As a conclusion, a technique for high precision temperature measurement based on an ANN model is proposed in this paper. The training process for MLP ANNs is performed successfully in this study with the use of LM algorithm which gives the best result among other learning algorithms. Gain and offset errors of the signal conditioning circuit are automatically cancelled as a consequence of the usage of the ANN technique. The proposed method has a large area of applications in all sensor based measurement systems where the sensor nonlinearity is the main factor to be considered. The technique has a potential future in the field of instrumentation and measurement.附录B高精度温度测量装置的设计1 引言 传感器是许多设备电路中最重要的元素之一。许多工业都应用传感器，传感器采用某一形势的输入（例如温度、压力、幅度等），并转换成能够解释的仪器指示数。在本质上许多传感器都是非线性的，但输出却要求是线性的。有许多种传感器可以进行温度的测量，其中热电偶的应用最广泛。由于热电偶具有低成本、宽操作范围、响应迅速、精确度高的优点，更适合工业应用。对于温度，热电偶也具有非线性输出。因此，传感器的仿真和线性化技术非常必要。为了解决传感器的线性化问题，提出了两种方法。第一种方法需要非线性的模拟电路，第二种方法应用能够用微处理器或计算机计算的数值方法。模拟电路经常被用来提高传感器的线性特性，但模拟电路需要额外的模拟硬件，还具有对于模拟电路的典型问题，如温度漂移，增益和补偿误差。应用第二种方法，如果一个精确的传感器是可用的（通过多项式直接计算），或应用查询多维的表格，传感器的非线性能够通过操作算法得到补偿。多项式方法的直接计算更加精确但需要较长的计算时间，而查表的方法尽管快但并不十分精确。 最近几年，在使用仪器和测量的领域，人工神经网络作为一种有前景的研究领域已经兴起。人工神经网络对于解决复杂问题特别是非线性系统的仿真提供了神经计算方法，而网络本身却是一个非线性系统。当测量系统是非线性时包括用来训练的试验数据也是非线性时，人工神经网络是非常有用的。人工神经网络的最广泛应用是数值逼近（曲线拟和）。与传统的数值插补比较，基于人工神经网络的插补提供低的插补误差。在本文中，我们提出了基于人工神经网络方法的高精度温度测量系统。校正数据是通过在人工神经网络的训练和测试阶段必须具有的调幅调频信号源9100校正单元获得的。系统的硬件和软件部分被综合在用于系统测量和校正的虚拟设备上。人工神经网络通过提供一个理想的最终误差来校正数据。这就是按照神经元的结构和层数和神经元的数量通过软件，将校正和人工神经网络仿真数据之间的均方误差最小化。2 系统硬件热电偶产生一和测量温度点成比例的在mV数量级的电压值，而在零点的温度值是一个常数。为了精确的测量，我们必须知道零点的温度值。通过人工神经网络的操作阶段，图1（a）显示了温度测量系统模块。它组成了放置在理想温度条件下的热电偶（E型号），包括带有16位的模拟数字转换器和接入到计算机的输入输接口卡的信号调节电路，设计的信号调节电路具有可设计增益的放（PGA204BP），它的增益是1，10，100，1000倍，16位的A/D转换器（AD976A），带有零点温度补偿的AD595单片电路热电偶放大器，并把它作为摄氏温度计的标准，用来选择热电偶和摄氏温度计的输出的4路模拟多路器（ADG529A）。AD976A具有高速度、低功耗、16位A/D转换器的特点，采用5V工作电压。这一部分提供连续的逼近，转换电容ADC，间隔的2.5V干扰和一高速度的平行界面。系统的精度直接依靠ADC每步的大小。具有的输入，AD976A的LSB是。当AD595作为摄氏温度应用时，热电偶被忽略，微分的输入被汇集到一起。在这种模式中，AD595产生一个比例因子为10mV/C，它的输出应用在编写软件的零点温度数据中。AD595的一些重要特性如下：电压范围是-55C;到125C;相对于温度的稳定性是0.05C/C；相对于温度的敏感性是：10mV/C。PGA204BP的输出信号通过AD976A被数化；AD976A的输出连接到I/O接口卡并传递到个人计算机上，执行数据的削减和优化。为了设定人工神经网络的网络权值和阈值，在校正阶段（人工神经网络的训练阶段），在000.000mV 到 320.000mV的范围内，精确度为0.006%+4.16A的调幅调频信号源9100校正单元，连接到模拟多路器的终端，用来产生如图1（b）所示的热电偶电压。此电压作为人工神经网络的输入值，没有零点温度补偿的热电偶电压是人工神经网络的输出值。在操作阶段（如图1a），为了实现零点温度补偿，我们使用了从摄氏温度计输出的数据值。人工神经网络的输出值随着由摄氏温度计获得的环境温度值的变化而变化。然后这个值显示在虚拟机上作为热电偶的温度值。虚拟机被用来获得人工神经网络的训练过程中的数据和显示操作阶段的计算温度值。图2显示了虚拟机的前端电路板。此设备的主要特征是：显示测量的温度值和从调理电路输出的相应电压值，调理电路是用来收集在校正阶段的数据值和在操作阶段具有零点温度补偿的实际电压值。此系统是由校正和操作阶段编写的软件来控制的。3 人工神经网络 人工神经网络是基于生物机理的人脑的模拟。人工神经网络是前向反馈网络和全局逼近网络。它是通过经验而不是程序来训练和学习的。它们是通过简单的进程元素相互连接而形成的，或是可调节的阈值相互连接而形成的，阈值组成神经的结构组成层。每一个人工神经都有阈值输入，求和单元、功能函数和输出。整个人工神经网络的特性依靠所提及的人工神经元、学习算法和网络的构造。在训练的过程中，神经元之间的阈值可以通过一些标准（