Saya mau tau Classification of measurement errors dari Sénsor. Jelaskan dalam bahasa sunda

A good sensor obeys the following rules:

the sensor should be sensitive to the méasured property

the sensor should be insensitive to any other property

the sensor should not influence the méasured property

idéal sensors are designed to be linear. The output signal of such a sensor is linéarly proportional to the value of the méasured property. The sensitivity is then defined as the ratio between output signal and méasured property. For example, if a sensor méasures temperature and has a voltage output, the sensitivity is a constant with the unit [V/K]; this sensor is linéar because the ratio is constant at all points of méasurement.

If the sensor is not idéal, 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 linéar.

Since the range of the output signal is always limited, the output signal will eventually réach a minimum or maximum when the méasured property exceeds the limits. The full scale range defines the maximum and minimum values of the méasured property.

If the output signal is not zero when the méasured 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 idéal 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 méasured 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 méasured 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 méasured property reverses direction, but there is some finite lag in time for the sensor to respond, créating 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 méasured 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 méasured. 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 méans 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.