Classification of measurement errors
A good sensor obeys the following rules:
the sensor should be sensitive to the measured property
the sensor should be insensitive to any other property
the sensor should not influence the measured property
Ideal sensors are designed to be linear. The output signal of such a sensor is linearly proportional to the value of the measured property. The sensitivity is then defined as the ratio between output signal and measured property. For example, if a sensor measures temperature and has a voltage output, the sensitivity is a constant with the unit [V/K]; this sensor is linear because the ratio is constant at all points of measurement.
If the sensor is not ideal, several types of deviations can be observed:
The sensitivity may in practice differ from the value specified. This is called a sensitivity error, but the sensor is still linear.
Since the range of the output signal is always limited, the output signal will eventually reach a minimum or maximum when the measured property exceeds the limits. The full scale range defines the maximum and minimum values of the measured property.
If the output signal is not zero when the measured property is zero, the sensor has an offset or bias. This is defined as the output of the sensor at zero input.
If the sensitivity is not constant over the range of the sensor, this is called nonlinearity. Usually this is defined by the amount the output differs from ideal behavior over the full range of the sensor, often noted as a percentage of the full range.
If the deviation is caused by a rapid change of the measured property over time, there is a dynamic error. Often, this behaviour is described with a bode plot showing sensitivity error and phase shift as function of the frequency of a periodic input signal.
If the output signal slowly changes independent of the measured property, this is defined as drift.
Long term drift usually indicates a slow degradation of sensor properties over a long period of time.
Noise is a random deviation of the signal that varies in time.
Hysteresis is an error caused by when the measured property reverses direction, but there is some finite lag in time for the sensor to respond, creating a different offset error in one direction than in the other.
If the sensor has a digital output, the output is essentially an approximation of the measured property. The approximation error is also called digitization error.
If the signal is monitored digitally, limitation of the sampling frequency also can cause a dynamic error.
The sensor may to some extent be sensitive to properties other than the property being measured. For example, most sensors are influenced by the temperature of their environment.
All these deviations can be classified as systematic errors or random errors. Systematic errors can sometimes be compensated for by means of some kind of calibration strategy. Noise is a random error that can be reduced by signal processing, such as filtering, usually at the expense of the dynamic behaviour of the sensor.