Important Process Instrumentation Terminology

In describing the characteristics and operation of process control instruments (such as process refractometers), it is very important to understand some common terms used in the industry. The definitions of some of the more common terms are provided below:

Accuracy: The closeness of an indicator or reading of a measurement device to the actual value of the quantity being measured; usually expressed as ± percent of the full scale output or reading.

Drift: The change in output or set point value over long periods of time due to such factors as temperature, voltage, and time.

Hysteresis: The difference in output after a full cycle in which the input value approaches the reference point (conditions) with increasing, then decreasing values or vice versa; it is measured by decreasing the input to one extreme (minimum or maximum value), then to the other extreme, then returning the input to the reference (starting) value.

Linearity: How closely the output of a sensor approximates a straight line when the applied input is linear.

Noise: An unwanted electrical interference on signal wires.

Nonlinearity: The difference between the actual deflection curve of a unit and a straight line drawn between the upper and lower range terminal values of the deflection, expressed as a percentage of full range deflection.

Precision: The degree of agreement between a number of independent observations of the same physical quantity obtained under the same conditions.

Repeatability: The ability of a sensor to reproduce output readings when the same input value is applied to it consecutively under the same conditions.

Resolution: The smallest detectable increment of measurement.

Sensitivity: The minimum change in input signal to which an instrument can respond.

Stability: The ability of an instrument to provide consistent output over an extended
period during which a constant input is applied.

Zero balance: The ability of the transducer to output a value of zero at the electronic null
point.

Understanding Error in Process Measurement

Instrumentation calibration is a procedure through which three general types of errors can be encountered. A typical signifier for a need of instrument recalibration is if the instrument is performing in an incorrect manner. This situation serves as a good way to showcase different types of error related to error analysis.

The three major category of errors regarding measurement are gross errors, systematic errors, and random errors. The first two categories of error, gross and systematic, are related to the two main elements of process control: controller and instrument. Gross errors are a product of the process controller or operator incorrectly evaluating the instrument value, with the best prevention of gross error being careful review of data both while recording and interpreting it.

Systematic errors impact every reading from a particular instrument, and are typically cause for instrument recalibration. Zero errors, where the instrument does not return to the predetermined zero value after each reading, are systematic errors because the same error in measurement is being displayed each time. Lastly, random errors impact instrumentation readings due to causes which are either unknown or simply unpredictable, meaning the error is both not able to be duplicated and is not a result of gross error. Random errors can be challenging to deduce due to both their singularity and their potential lack of a clear cause.

The previously mentioned zero error, also known as a zero shift calibration error, is typified by the resulting readings being offset at the same percentage. For example, a pressure transmitter which is functioning incorrectly as the result of a zeroing error can be corrected by a corresponding zero adjustment. After the adjustment and the transmitter being calibrated back to the correct zero point, the error will disappear. Another common type of systematic error is span shift calibration error. Unlike the zero error, span error can impact readings from the instrument repeatedly, but not necessarily identically. Similarly, by correcting the corresponding setting on the transmitter, in this case the span adjustment, the instrument can be correctly programmed once again by measuring the readings against a properly configured reference.

Hysteresis error occurs when the instrument in question returns erroneous responses as the input variable changes. The antidote to this kind of systematic error is to check the instrument against a pre-defined set of calibration points, first by increasing the input, and then subsequently decreasing the input in sequence to determine how the instrument responds as the input changes. Mechanical friction has been known to be a common culprit for hysteresis errors.

Understanding the capabilities and limitations of whatever instrument is relied upon for delivering process information is essential to successful operation.

Introduction to Industrial Instrumentation

Engineer adjusting a
process controller measuring 
the refractive index of a process.

Instrumentation is the science of automated measurement and control. Applications of this science abound in modern research, industry, and everyday living. From automobile engine control systems to home thermostats to aircraft autopilots to the manufacture of pharmaceutical drugs, automation surrounds us. This chapter explains some of the fundamental principles of industrial instrumentation.

The first step, naturally, is measurement. If we can’t measure something, it is really pointless to try to control it. This “something” usually takes one of the following forms in industry:

• Fluid pressure
• Fluid flow rate
• The temperature of an object
• Fluid volume stored in a vessel
• Chemical concentration
• Machine position, motion, or acceleration
• Physical dimension(s) of an object
• Count (inventory) of objects
• Electrical voltage, current, or resistance
• Refractive Index

Once we measure the quantity we are interested in, we usually transmit a signal representing this quantity to an indicating or computing device where either human or automated action then takes place. If the controlling action is automated, the computer sends a signal to a final controlling device which then influences the quantity being measured.

This final control device usually takes one of the following forms:

• Control valve (for throttling the flow rate of a fluid)
• Electric motor
• Electric heater

Both the measurement device and the final control device connect to some physical system which we call the process. To show this as a general block diagram:

Process control loop

The common home thermostat is an example of a measurement and control system, with the home’s internal air temperature being the “process” under control. In this example, the thermostat usually serves two functions: sensing and control, while the home’s heater adds heat to the home to increase temperature, and/or the home’s air conditioner extracts heat from the home to decrease temperature. The job of this control system is to maintain air temperature at some comfortable level, with the heater or air conditioner taking action to correct temperature if it strays too far from the desired value (called the setpoint).

Industrial measurement and control systems have their own unique terms and standards. Here are some common instrumentation terms and their definitions:

Process: The physical system we are attempting to control or measure. Examples: water filtration system, molten metal casting system, steam boiler, oil refinery unit, power generation unit.

Process Variable, or PV: The specific quantity we are measuring in a process. Examples: pressure, level, temperature, flow, electrical conductivity, pH, position, speed, vibration.

Setpoint, or SP: The value at which we desire the process variable to be maintained at. In other words, the “target” value for the process variable.

Primary Sensing Element, or PSE: A device directly sensing the process variable and translating that sensed quantity into an analog representation (electrical voltage, current, resistance; mechanical force, motion, etc.). Examples: thermocouple, thermistor, bourdon tube, microphone, potentiometer, electrochemical cell, accelerometer.

Example of a transducer.
In this case, a
Refractive Index transducer.

Transducer: A device converting one standardized instrumentation signal into another standardized
instrumentation signal, and/or performing some sort of processing on that signal. Often referred to as a converter and sometimes as a “relay.” Examples: I/P converter (converts 4- 20 mA electric signal into 3-15 PSI pneumatic signal), P/I converter (converts 3-15 PSI pneumatic signal into 4-20 mA electric signal), square-root extractor (calculates the square root of the input signal).
Note: in general science parlance, a “transducer” is any device converting one form of energy into another, such as a microphone or a thermocouple. In industrial instrumentation, however, we generally use “primary sensing element” to describe this concept and reserve the word “transducer” to specifically refer to a conversion device for standardized instrumentation signals.

Transmitter: A device translating the signal produced by a primary sensing element (PSE) into a standardized instrumentation signal such as 3-15 PSI air pressure, 4-20 mA DC electric current, Fieldbus digital signal packet, etc., which may then be conveyed to an indicating device, a controlling device, or both.

Example of a transmitter and/or
controller. In this case, refractive
index signal conditioning electronics
to modify the transducer signal,
and optionally, provide a control
output to a final control element.

Lower- and Upper-range values, abbreviated LRV and URV, respectively: the values of process ^oC and its URV would be 500 ^oC.
measurement deemed to be 0% and 100% of a transmitter’s calibrated range. For example, if a temperature transmitter is calibrated to measure a range of temperature starting at 300 degrees Celsius and ending at 500 degrees Celsius, its LRV would be 300

Zero and Span: alternative descriptions to LRV and URV for the 0% and 100% points of an instrument’s calibrated range. “Zero” refers to the beginning-point of an instrument’s range (equivalent to LRV), while “span” refers to the width of its range (URV − LRV). For example, if a temperature transmitter is calibrated to measure a range of temperature starting at 300 degrees Celsius and ending at 500 degrees Celsius, its zero would be 300 ^oC and its span would be 200 ^oC.

Controller: A device receiving a process variable (PV) signal from a primary sensing element (PSE) or transmitter, comparing that signal to the desired value (called the setpoint) for that process variable, and calculating an appropriate output signal value to be sent to a final control element (FCE) such as an electric motor or control valve.

Final Control Element, or FCE: A device receiving the signal output by a controller to directly influence the process. Examples: variable-speed electric motor, control valve, electric heater.

Manipulated Variable, or MV: The quantity in a process we adjust or otherwise manipulate in order to influence the process variable (PV). Also used to describe the output signal generated by a controller; i.e. the signal commanding (“manipulating”) the final control element to influence the process.

Reprinted from Lessons In Industrial Instrumentation by Tony R. Kuphaldt under the terms and conditions of the Creative Commons Attribution 4.0 International Public License.