A sensor is a detection device that can sense the information being measured and transform the sensed information into an electrical signal or other required form of information output according to certain rules, so as to meet the requirements of information transmission, processing, storage, display, recording and control.
What are the main characteristic indicators of a sensor?
I. Sensor Static
The static characteristics of a sensor refer to the relationship between the sensor's output and input signals when the input signal is static. Because both the input and output signals are independent of time, their relationship—the sensor's static characteristics—can be described by an algebraic equation without a time variable, or by a characteristic curve plotted with the input signal on the x-axis and the corresponding output signal on the y-axis. The main parameters characterizing the static characteristics of a sensor include linearity, sensitivity, hysteresis, repeatability, and drift.
(1) Linearity: refers to the degree to which the actual relationship curve between the sensor output and input deviates from the fitted straight line. It is defined as the ratio of the maximum deviation between the actual characteristic curve and the fitted straight line over the full range to the full-scale output value.
(2) Sensitivity: Sensitivity is an important indicator of the static characteristics of a sensor. It is defined as the ratio of the increment of the output quantity to the corresponding increment of the input quantity that causes that increment. Sensitivity is represented by S.
(3) Hysteresis: The phenomenon that the input and output characteristic curves of a sensor do not coincide during the period when the input quantity changes from small to large (forward stroke) and from large to small (reverse stroke) is called hysteresis. For the same input signal, the output signal of the sensor during the forward and reverse strokes are not equal, and this difference is called the hysteresis difference.
(4) Repeatability: Repeatability refers to the degree of inconsistency in the characteristic curves obtained when the input quantity of a sensor changes continuously multiple times in the same direction across the entire range.
(5) Drift: Sensor drift refers to the change in sensor output over time when the input remains constant. This phenomenon is called drift. There are two main causes of drift: one is the sensor's own structural parameters; the other is the surrounding environment (such as temperature, humidity, etc.).
(6) Resolution: When the input of a sensor increases slowly from a non-zero value, the output changes observably after exceeding a certain increment. This input increment is called the resolution of the sensor, i.e., the minimum input increment.
(7) Threshold: When the input of the sensor slowly increases from zero, the output changes observably after reaching a certain value. This input value is called the threshold voltage of the sensor.
II. Sensor Dynamics
Dynamic characteristics refer to the output characteristics of a sensor when the input changes. In practical applications, the dynamic characteristics of a sensor are often represented by its response to certain standard input signals. This is because the sensor's response to standard input signals is easily obtained experimentally, and there is a certain relationship between its response to standard input signals and its response to arbitrary input signals; often, knowing the former allows us to infer the latter. Commonly used standard input signals include step signals and sinusoidal signals, so the dynamic characteristics of sensors are often represented by step response and frequency response.
III. Linearity
Typically, the actual static characteristic output of a sensor is a curve rather than a straight line. In practical applications, to ensure that the instrument has a uniformly calibrated reading, a fitted straight line is often used to approximate the actual characteristic curve. Linearity (non-linear error) is a performance indicator of the degree of this approximation.
There are several methods for selecting the fitting line. For example, the theoretical line connecting the zero input and full-scale output points can be used as the fitting line; or the theoretical line that minimizes the sum of the squares of the deviations from the characteristic curve can be used as the fitting line. This fitting line is called the least squares fitting line.
IV. Sensitivity
Sensitivity refers to the ratio of the change in output (Δy) to the change in input (Δx) of a sensor under steady-state operation. It is the slope of the output-input characteristic curve. If the sensor's output and input have a linear relationship, then the sensitivity S is a constant. Otherwise, it will vary with the input.
Sensitivity is measured by the ratio of the dimensions of the output and input quantities. For example, a displacement sensor whose output voltage changes by 200mV when the displacement changes by 1mm has a sensitivity of 200mV/mm. When the dimensions of the sensor's output and input quantities are the same, sensitivity can be understood as the amplification factor. Increasing sensitivity leads to higher measurement accuracy. However, higher sensitivity often results in a narrower measurement range and poorer stability.
V. Resolution
Resolution refers to a sensor's ability to detect the smallest change in a measured quantity. In other words, if the input quantity changes slowly from a non-zero value, the sensor's output will not change if the change in input does not exceed a certain value; that is, the sensor cannot distinguish this change in input quantity. Only when the change in input quantity exceeds the resolution will its output change.
Typically, the resolution of a sensor varies across its full-scale range. Therefore, the maximum change in the input quantity that causes a step change in the output within the full-scale range is commonly used as a metric for resolution. This metric, expressed as a percentage of the full-scale range, is called resolution. Resolution is negatively correlated with sensor stability.
