What are smart sensors?


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What is a smart sensor?

A sensor that has local processing power that enables it to react to local conditions without having to refer back to a central controller is called smart sensor.

Smart sensors are usually at least twice as accurate as nonsmart devices, have reduced maintenance costs, and require less wiring to the site where they are used. In addition, long-term stability is improved, reducing the required calibration frequency.

Functions of a Smart sensor:

  • Calibration capability:

Self-calibration is very simple in some cases. Sensors with an electrical output can use a known reference voltage level to perform autocalibration. In addition, the types of load cells of the sensors, which are used in the weighing systems, can adjust the output reading to zero when there is no applied mass.

In the case of other sensors, two self-calibration methods are possible: the use of a look-up table and an interpolation technique. Unfortunately, a lookup table requires a large memory capacity to store correction points. In addition, a large amount of sensor data must be collected during calibration.

  • Self-diagnosis of faults:

Intelligent sensors perform a self-diagnosis by monitoring internal signals for evidence of faults. While it is difficult to achieve a sensor that can perform a self-diagnosis of all possible faults that may arise, it is often possible to perform simple checks that detect many of the most common faults.

One difficulty that often arises in self-diagnosis is the differentiation between normal measurement deviations and sensor faults. Some smart sensors overcome this by storing multiple measured values around a set point and then calculating the minimum and maximum values expected for the measured quantity.

  • Automatic calculation of measurement accuracy and compensation for random errors:

Many intelligent sensors can calculate the accuracy of online measurements by calculating the average over a series of measurements and analyzing all the factors that affect the accuracy. This averaging process also serves to greatly reduce the magnitude of random measurement errors.

  • Adjustment for measurement nonlinearities:

In the case of sensors that have a non-linear relationship between the measured quantity and the sensor output, digital processing can convert the output to a linear form, provided that the nature of the non-linearity is known, so that an equation that the description can be programmed in the sensor.