外文翻译----多传感器的自我测量诊断系统.docx

附录AA Multi-Sensor Based TemDerature Measuring System with Self-Diagnosis Abstract- A new multi-sensor based temperature measuring system with self-diagnosis is developed to replace a conventional system that uses only a single sensor. Controlled by a 16-bit microprocessor, each sensor output from the sensor array is compared with a randomly selected quantised reference voltage at a voltage comparator and the result is a binary “one” or “zero”. The number of “ones” and “zeroes” is counted and the temperature can be estimated using statistical estimation and successive approximation. A software diagnostic algorithm was developed to detect and isolate the faulty sensors that may be present in the sensor array and to recalibrate the system. Experimental results show that temperature measurements obtained are accurate with acceptable variances. With the self- diagnostic algorithm, the accuracy of the system in the presence faulty sensors is significantly improved and a more robust measuring system is produced. Index Terms-Instrumentation and Measurement, Sensors.Transducers I. INTRODUCTION Conventional sensing system uses a single sensor to convert a measured into an electric signal. There is no built-in redundancy and the system is wholly dependent on the single sensor for its accuracy. Recently, a novel approach proposed by the author in l makes use of the principles of successive approximation and statistical estimation to provide a simple yet accurate estimate of the measured with only a small number of sensors. Replacing the single sensor with a multi-sensor array also improves the robustness of the system reducing system dependency on any single sensor. The system is still functional even with a few faulty sensors, though there will be a degradation in the accuracy of the results. To overcome the degradation in the accuracy due to the presence of faulty sensors, a self-diagnostic algorithm is devised to determine and isolate faulty sensors so that these sensors are not used in the determination of the temperature estimate. In this paper, the development of such concept into a practical system for temperature measurement is described. II. SYSTEM ARCHITECTURE AND OPERATION A. System Hardware Architecture The hardware system consists of 36 temperature sensors in a ensure array, a signal conditioning circuit and a 16-bit micro- controller, as shown in Fig. 1. Each sensor, controlled by an analog switch, measures temperature and outputs a voltage. The output from all 36 sensors are fed into a switching circuit. The switching circuit consists of a decoder and an analog multiplexer that is controlled by the software to sequentially select an output from all the 36 sensors. The selected output is fed into the signal conditioning circuit for processing before being sent to the microcontroller. One complete “read cycle” involves reading the outputs from all 36 sensors. The sensors used in the sensor array are calibrated beforehand to obtain their voltage-temperature characteristic. The aggregate voltage-temperature relationship for the sensor array was found to be linear over the temperature range to be measured, thus a simple linear equation is used in the software algorithm to convert the voltage reading into a temperature reading. B. Temperature measurement To obtain an estimate of the output temperature, mathematical principles of successive approximation and statistical estimation are used. The analog sensor output are sequentially selected by the switching circuit and passed onto the non-inverting input of a voltage comparator for digitization. A reference voltage that is determined by the software program is applied to the inverting input of the voltage comparator. If the analog sensor voltage is higher than the reference voltage then the output at the comparator is a binary “one”, else the result is a binary “zero”. The initial reference voltage range of is established based on apriority knowledge of the characteristics of the temperature sensors and the temperature range to be measured. The voltage range is then quantized into m different levels with an equal step sue of where m is the number of sensors in the sensor array and represents the maximum and minimum value of the initial voltage range before any successive approximation is carried out. The m reference voltages are randomly sorted. For each reading from the sensor array, a quantized reference voltage is randomly selected for comparison at the voltage comparator. This is to reduce the dependency of any sensor reading to the reference voltage applied. At the output of the comparator, a binary “one” or “zero” is produced. The quantized reference voltage is generated by the software algorithm and converted into an analog voltage through a 12- bit digital-to-analog converter PAC. One complete “read cycle” involves processing the analog sensor voltages him all 36 sensors to obtain 36 binary readings. The binary output from the comparator is fed to the microcontroller for so hare processing. The microcontroller counts the number of binary “ones” in a read cycle. Based on the number of “ones”, statistical estimation is used to obtain the temperature estimate V as follows. If the accuracy of the estimate, given by Arid, does not meet a predetermined level, successive approximation is carried out to reduce or narrow down the reference voltage range. The new reference voltage range for the new “read cycle” is given . where V is the current estimate of the sensor output, Avert ,o is the current quantized voltage step size and k is an integer that controls the next reference voltage range (in this case, k = 2). The new voltage range is again quantized into m levels with a voltage step sue of Avail and another “read cycle” is carried out to obtain a new estimate of the sensor output, The successive approximation is carried out until the required accuracy is obtained. In general, the reference voltage range, step size and the estimated sensor voltage output after the successive approximation (i = 1,2,.) are given by: The final voltage estimate is converted into a temperature reading using the conversion formula obtained from the initial calibration. Apart from calculating the temperature estimate, the software process is also responsible for synchronizing the various hardware components to ensure that the sensor readings are processed in the correct order. The flowchart in Fig. 2 shows the software algorithm for temperature measurement. Overall, the system works by digitizing the analog signals from each sensor in the array. Statistical estimation is used to obtain a first approximation of the temperature. Successive approximation is then applied based on the estimate to reduce the voltage range until the desired accuracy is met. The process is repeated until the temperature measurement with a desired accuracy is obtained. III. SELF-DIAGNOSIS The self-diagnosis algorithm is a software controlled procedure to detect whether any of the sensors in the array is faulty, to isolate and deactivate any faulty sensors present and to compensate for the faulty sensors. The diagnosis assumes that the majority of the sensors in the array are in good order. A sensor is classified as faulty if its measurement is more than x from the actual temperature .where x is a user defined value depending on the temperature sensor used and the accuracy required. In the prototype. In diagnosis, the same reference voltage is applied to the voltage comparator for all the sensor outputs in a “read cycle. In principle, all the sensors are expected to produce the same digital out& with two exceptions: when the reference voltage is very close to the voltage corresponding to the actual temperature ; if the sensor is faulty and gives rise to an inaccurate output different film those of the majority sensors. Thus as the reference voltage applied to the comparator is shifted between him V (min) to V (max), it should be able to separate the good and faulty sensors. This is illustrated in Fig. 3 where, as indicated, sensor 20 and 26 are filly. The diagnostic algorithm works as follows. At a certain temperature, a constant reference voltage is applied to the comparator for all the sensors in the array. The total number of “ones” and “zeros” are counted and the majority state or “zero” is determined. A sensor that belongs to the minority state is likely to be faulty. Each sensor has a software status counter associated with it that is initially set to zero at the start of the diagnostic routine. This counter is incremented if the sensor was found to belong to the minority state. Incrementing the status counter of a sensor indicates a high probability that the sensor is faulty. In Fig. 3 both sensor 20 and 26 will have their software counters incremented for the V, level since they belong to the minority state. The constant reference voltage is shifted between the extreme ends of the voltage range through scanning (moving from V(min) to V (max) in fixed increments). For each diagnostic reference voltage applied, all sensor inputs are scanned (one “read cycle”). After the entire voltage range has been scanned, if the software counter of any sensor has been incremented by more than three times (in the prototype, this is equivalent to a 4 deviation from the actual temperature), then the sensor is deemed faulty and is deactivated. The faulty sensor is deactivated through software by discarding the binary reading produced by the faulty sensor at the comparator that is read in by the microcontroller. Progressively shifting the diagnostic reference voltage from V (min) to V(max) can be very time consuming. For example, if the initial voltage range is 1.OV and the step increment is 1.0V then 100 diagnostic reference voltages (“read cycles”) need to be applied. To reduce the processing time of the diagnostic algorithm, successive approximation is used. The initial diagnostic reference voltage applied is in the mid value of the initial voltage range, i.e. diagnostic scan is carried out. If the results from the “read cycle” show that there are more “ones” than “zeroes”, then the non-faulty sensors lie above V and the next voltage range is from V to V (max). If the reverse is true, i.e. more “zeroes” than “ones”, then the non-faulty sensors lie below V and the next voltage range is from V(min) to V. second successive approximation is used to further reduce the range of the diagnostic reference voltage. After 2 stages of successive approximation, the initial voltage range has been subdivided into 4 regions (quartiles) and the software algorithm is able to determine which quartile does the majority of the sensors fall into. The upper and lower limit of the diagnostic voltage range of the quartile where the majority of the sensor readings are located is extended by 3 times the diagnostic incremental size to take care of boundary conditions. Boundary conditions occur during the successive approximation stages, when the sensor voltage readings are clustered close to the diagnostic reference voltages (V- , V+) and the number of “ones” and “zeroes” cannot be accurately determined. Within this reduced diagnostic voltage range, the diagnostic reference voltage is sequentially incremented to detect the faulty sensors. Using the earlier example, it is given that the initial voltage range is 1.0V and the incremental step size is 0.01V and assuming that the surrounding temperature correspond to a voltage reading of 0.6V. In the less successive approximation, the diagnostic voltage range is reduced to 0.5V, 1.0V. After the 2 successive approximation, the diagnostic voltage range is 0V, 0.75Vl. The limits of the voltage range are extended because of boundary conditions to 0.47V, 0.78VI and progressive scanning is carried out. Thus the diagnosis will take 2 + (78- 47) = 33 cycles to complete instead of the original 100 cycles. Thus by using successive approximation, the voltage range to be scanned can be reduced drastically. IV. EXPERIMENTAL, RESULTS A prototype of the temperature measuring system was constructed using a MCB251 16-bit microprocessor and 36 LM35DZ temperature sensors that have an individual accuracy off 1 The system was then tested in an oven over a temperature range film 45 to 60 . The results are shown in Table I. It can be seen that very accurate results over the temperature range of 45 to 60 are obtained. The maximum error is 0.05”C. This shows that the multi- sensor system is able to provide an improvement to the accuracy of the temperature estimate compared to the single sensor system. Next faulty sensors are deliberately introduced into the system. Two types of faulty sensors are introduced: “faulty- OW (outputs OV) and “faulty-high” sensors (outputs VCC).The middle column of Table TI shows the effect on the measured temperature when 2,4 or 6 faulty-haymow sensors are introduced into the system at 25C. The accuracy of the temperature measurement decreases as the number of faulty temperature sensors increases. The system exhibits a degree of robustness at the presence of faulty sensors at the expense of degradation in the accuracy of the temperature measurement. CONCLUSION A multi-sensor based temperature-measuring system with self diagnosis is described. Based on successive approximation and statistical estimation, the system is able to produce accurate temperature measurement using a small number of sensors. With a multi-sensor array, the system exhibits a certain degree of redundancy although the accuracy is degraded when faulty sensors exist. A diagnostic algorithm is developed to identify% the faulty sensors and subsequently deactivate them. With the diagnostic algorithm, the system is able to isolate the faulty sensors and produce an accurate temperature measurement. Unlike the conventional sensing system, the proposed system utilizes a small number of sensors to determine the measured, based on the principle of successive approximation and statistical estimation. The system has a built in redundancy, that improves the robustness of the measurement. Finally, the principle applied in this system is generic, and can easily be adjusted to measure other properties. 附录B多传感器的自我测量诊断系统摘要一个新的传感器的温度测量系统的自我诊断、开发，以取代传统的系统只使用一个单一的传感器。由16位微处理器，每个传感器输出的传感器阵列是与随机选取的参考电压进行电压比较，结果是一个二进制的“ 1 ”或“0” 。“0”和“1”的计算使用统计估计和逐次逼近方法。软件诊断算法是发达国家来检测和隔离故障的应用于传感器的算法，可以用在传感器阵列，并可重新调整系统。 实验结果表明，温度测量获得准确数据，与自我诊断算法的准确性，及系统是否存在故障传感器密切相关。导言传统的传感系统使用一个传感器转换测量成电信号。不存在内置冗余和系统的完全依赖单传感器的精度。最近，一种利用一种原则提出新的方法， 逐次逼近和统计估计提供简单而准确的估计的测量，只用少量的传感器，取代了传统只用单一的传感器的方法， 多传感器阵列还提高了系统的准确性，降低了系统依赖于任何单一传感器，甚至那个系统有一个故障的传感器。为了克服由于存在故障的传感器系统准确性的问题，自我诊断算法是设计一种方法，以确定和隔离故障的传感器，使这些传感器不被用来测定温度数值。本文的发展，这种概念纳入实际系统的温度测量中。系统架构和运行他的硬件系统由36个温度传感器在恩索尔阵列的信号调理电路和一个16位微控制器所组成 。每个传感器，有专门的控制的开关和输出电压。 所有的输出36传感器输入开关电路。开关电路由一个解码器和一个模拟多路控制软件按顺序选择一个输出。那个选定的输出输入信号调理电路处理前被发送到微控制器。一个完成校准的传感器用于传感器阵列的校准，事先获得其电压温度特性。其一系列数据被认为是线性的温度数据，才能在一个方程中使用，用软件算法转换电压读成温度读数。温度测量要获得的估计输出温度， 数学原则，逐次逼近和 统计估计方法。模拟传感器输出的 关于选定的顺序开关电路和转嫁 非反相输入电压比较。参考电压是确定的 ，软件程序是用于反相输入的 电压比较。如果模拟传感器电压高比参考电压那么输出的比较结果是二进制“ 1 ” ，否则其结果是一个二进制“0” 。 基于Apriority 的特点，用温度传感器和温度范围来衡量电压范围 。然后根据quantized不同层次的步骤 ，说明的其中那些是一些传感器 、传感器阵列和代表 最高和最低值的初始电压范围 ，这些都要在逐次逼近之前进行。对于每个 传感器阵列，一个参考电压 是随机挑选的比较在电压进行 比较。这是为了减少依赖任何传感器参考电压。输出的比较值，是二进制“ 1 ”或“0”。那个 参考电压生成软件 算法和转换成模拟电压通过一个12 - 位数字到模拟转换器 。一个完整的周期及处理模拟传感器电压所有36传感器，从而获得36位二进制数据。 二进制输出比较是交给 微控制器的shore处理