The Sensor characteristic curve describes the functional relationship between a physical quantity and the electrical output signal generated by the sensor. It graphically illustrates how a sensor responds to changes in a physical quantity and enables the precise conversion of real process values into analyzable measurement data.
Typically, the x-axis represents the measured quantity—such as temperature, pressure, force, level, vibration, or flow—while the y-axis represents the corresponding sensor signal in volts (V), millivolts (mV), milliamps (mA), resistance (Ω), or frequency.
A classic example is the Temperature measurement: As the temperature rises, a temperature sensor changes its electrical output signal. The resulting calibration curve shows the relationship between the measured value and the sensor signal. Ideally, this calibration curve is linear, so that any change in the measured variable results in a proportional change in the output signal.
In practice, however, sensor calibration curves are often affected by various factors. These include:
- Nonlinear Sensor Characteristics
- Ambient temperatures
- Electrical Interference and EMC Effects
- Sensor Aging
- Line Resistances
- Installation Configuration and Process Conditions
To ensure high measurement accuracy, therefore, Characteristic Curve Corrections, Linearizations or Sensor Compensations performed. Modern measurement data acquisition systems enable the automatic conversion and correction of sensor signals directly during data acquisition.
The measurement and data acquisition solutions from Delphin Technology support the precise acquisition of a wide variety of sensor signals, such as mV, mA, thermocouple, Pt100, frequency, and vibration signals. Through intelligent signal processing, galvanically isolated inputs, and powerful software, sensor characteristics can be reliably acquired, visualized, analyzed, and evaluated for demanding measurement and test applications. This enables high measurement quality in laboratories, test benches, industrial processes, and monitoring applications.
Why is the sensor characteristic curve important?
The sensor characteristic curve forms the basis for:
- Precise Measurement Data Acquisition
- Calibration and Validation of Sensors
- Comparison of Different Sensor Types
- Fault Analysis and Quality Assurance
- Automated Process Monitoring
- Reliable Test Bench and Laboratory Applications
Only when the calibration curve is known and correctly taken into account can measured values be accurately converted into the actual physical quantity.