Measurement Analysis of Sensor Components–An Application of Temperature Sensing

• Main Authors: Jia-Sian Jian, 簡嘉賢
• Other Authors: Chien-Tung Yang
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/22110984390567742977

碩士 === 國立臺灣海洋大學 === 系統工程暨造船學系 === 99 === Sensor Device Data Acquisition requires Analog-to-Digital Converters of efficient and satisfactory functionality. There exists an ideal relationship for any kind of Analog-to-Digital Converters. However, the Analog-to-Digital Converters show non-ideal characteristic in practical application, and this may lead to unavoidable errors between the measured readings and the actual converted data. There are many causes of error including nonlinearity and zero offset. Also, the nose is usually one of the sources to cause error in the Converters. Noise output can cause subsequent malfunction phenomenon in the circuit or components. Many factors contribute to the production of noise, including external factors and noises resulted from internal operation of the Converters. The noises caused by external factors can be eliminated by means of filters and grounding. The noise resulted from internal operation of the Converters cannot be simply removed by eliminating the external circuits. Based on the integration of above described considerations, it is therefore important to choose appropriate Analog-to-Digital Converters in order to obtain accurate results in case of data acquisition. Temperature Sensor has a wild range of application; in general the temperature sensing devices usually convert the temperature readings to signals easily processable for the system, and the Analog-to-Digital Converters work as the bridge connecting the temperature sensor and the system. In this research the temperature sensors used are Thermocouples and Resistance Temperature Detector. To understand the non-ideal characteristic by testing two different types of Analog-to-Digital Converter in actual measurement, it is hoped to better improve the quality of data acquisition in the future.