alexa Sensor Data Acquisition, Manipulation, and Transmission | OMICS International
ISSN: 2167-7670
Advances in Automobile Engineering
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

Sensor Data Acquisition, Manipulation, and Transmission

Shaopeng (Frank) Cheng*
School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan 48859,USA
*Corresponding Author : Shaopeng (Frank) Cheng
School of Engineering and Technology
Central Michigan University
Mount Pleasant, Michigan 48859,USA
E-mail: [email protected]
Received August 25, 2013; Accepted August 27, 2013; Published August 30, 2013
Citation: Cheng S (2013) Sensor Data Acquisition, Manipulation, and Transmission. Adv Automob Eng 1:e114. doi: 10.4172/2167-7670.1000e114
Copyright: © 2013 Cheng S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Advances in Automobile Engineering

Abstract

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 nonlinearity 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
Signal conditioning circuits have to be used for resistive sensors to generate the output voltage. This requirement also provides the chance of reducing the degrees of sensor nonlinearity. For example, parallelizing a nonlinear resistive sensor like a thermistor to a specified constant resistor can improve the nonlinearity of sensor output resistance. This technique is called the “resistor mode” of linearization. A simple voltage divider circuit can also reduce the degree of nonlinearity of a nonlinear resistive sensor when it is used within a small measuring range. This application is called the “voltage mode” of linearization. Complex signal conditioning circuits and software algorithms have been developed to establish sensor linearity. In general, the most important drawback of software based linearization methods is the introduction of measurement delay, which is particularly the concern when the sensor is used in the feedback path of a closed loop control system. The best approach is to integrate both hardware based linearizer and software based simple algorithm to a two-tier structure of linearization arrangement. In this case, a signal conditioning circuit may be used to accomplish the first stage of linearization by improving the linearity to a certain extent. The resulting signal is further linearized by a simple algorithm to achieve the second stage linearization.
As another popular signal conditioning circuit, the Wheatstone bridge circuit is used for converting the small resistance change produced by a resistive sensor such as a thermistor or strain gauge into the corresponding voltage. Among the useful bridge circuit configurations, the single-element varyingbridge is most suited for temperature sensing with a thermistor, and the all-element varyingbridge (i.e. full bridge) is commonly used in strain gauge applications due to its linearity.
In the sensor “data manipulation” process, the signal conditioning circuit can also be designed to linearly adjust the sensor output from a small range of Amin ≤ Aout ≤ Amax to a large range of Bmin ≤ Bin ≤ Bmax. It can be modeled as Bout = KAin + B0 , where K is the amplification gain and B0 is the output offset which can be either positive ( B0 > 0 ) or negative ( B0 < 0 ). Different operational amplifier (op-amp) circuits can be used to implement the required adjustment. For example, to implement the voltage-to-voltage adjustment of Vout =GVin +V0 , an inverting operational amplifier can be configured as a summing operational amplifier with the gain G. Besides the use of a summing operational amplifier, an instrumentation amplifier can also be used to implement the voltage adjustment of Vout =G(Vin −V'0 ) , where V0 =V'0 /G .
The measurement accuracy of a resistive sensor is concerned with how the sensor output voltage is generated, transmitted, and used. One of the most important concerns in analog signal conditioning is the loading of one circuit by another. This introduces uncertainties on sensor output voltage Vx as it is passed through the measurement process. For example, the sensor output voltage will drop to some value Vy<Vx after the sensor connects to a “loading” circuit. Different “loading” may result in different voltage drops. This effect can be explained by the application of the Thevenin’s theorem in a sensor circuit. According the Thevenin’s theorem the sensor output voltage Vx can be modeled as a voltage source Vx that is in series with an equivalent Thevenin’s output resistance Rx determined by the sensor output resistance. Because of the existence of Rx any current at the sensor output terminal will cause a voltage drop over Rx, which results in a drop of the senor output voltage. The best way to prevent this problem is to use an op-amp based voltage buffer called “voltage follower.” An ideal voltage buffer is a unit-gain operational amplifier circuit that has infinite input resistance and zero output resistance. If such a voltage buffer connects to the sensor output circuit, the infinite input resistance of the buffer makes the sensor output circuit an open circuit, resulting in no change of the sensor output voltage as there is no current at the sensor terminals. Also, the zero output resistance of the buffer allows the senor to be the loaded by any “loading” circuit without having any change on the sensor output voltage.
Besides the “loading” effect, the wire resistance existed in sensor “data transmission” will also alter the sensor output voltage. The common solution to this problem is through converting the sensor output voltage of Vmin ≤Vout ≤Vmax into the industry-standard current output of 4 mA ≤ Iout ≤ 20 mA. This linear conversion from the sensor output voltage Vout to the corresponding output current Iout can be implemented by using the operational amplifier circuit called voltageto- current converter.
Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Article Usage

  • Total views: 15703
  • [From(publication date):
    December-2013 - Jan 20, 2019]
  • Breakdown by view type
  • HTML page views : 11876
  • PDF downloads : 3827
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2019-20
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri and Aquaculture Journals

Dr. Krish

[email protected]

+1-702-714-7001Extn: 9040

Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001Extn: 9040

Clinical Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

Food & Nutrition Journals

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

General Science

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics & Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Materials Science Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Nursing & Health Care Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

Ann Jose

ankara escort

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

pendik escort

[email protected]

1-702-714-7001Extn: 9042

 
© 2008- 2019 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
Leave Your Message 24x7