alexa Mechanical Engineering Innovations|Omics Group|Journals|Advances In Automobile Engineering

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Mechanical Engineering Innovations

In engineering systems there are variables that need to be monitored and regulated for optimum performance. For mechanical and fluid systems, the most important variables are position, velocity, force, torque, temperature, flow rate, and pressure. In general, sensors and instruments are used to measure the physical quantities of the system (or process) in real-time. There are three basic processes involved in sensor measurements. The senor data acquisition process is about converting the measured physical variables into the electrical quantities such as resistance, capacitance, or inductance through properties of special materials like a diaphragm or metallic wire. The sensor data manipulation process uses the converted electrical quantity to generate the corresponding electrical voltage (or current) for representing the sensor measurement. The final process is the sensor data transmission in which the sensor measurement represented by the output voltage (or current) is to be physically sent to the control room that might be away from the location of the senor measurement. Sensor application and sensor design are two closely related processes. Sensor application starts with choosing an available sensor that is able to measure the unknown variable such as force, temperature, and pressure required in engineering systems as the electrical voltage (or current). Sensor design starts with specifying sensor variables, ranges, and functions, and then implements them with physical materials and electrical circuits. Equations must be established to represent the relationships of all involved variables. Usually, one set of sensor equations is used in sensor application and the inverse of the same set of equations is used in sensor design. Linearity and accuracy are the two main objectives in sensor design and application. Often, nonlinear relationships exist in both sensor data acquisition and data manipulation processes. Linear approximation is commonly made for nonlinear sensors while maintaining the measurement accuracy. For some nonlinear sensors, high degrees of non linearity occur as the input variable is measured in a large range. But if the sensor is only used for measuring a small range of the input variable, linear approximation techniques can be effectively applied to meet the required accuracy.[Cheng S (2013) Sensor Data Acquisition, Manipulation, and Transmission]
 
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