In many cases modern inclination sensors use the MEMS technology for it’s sensor elements. Repeatability and linearity is not the challenge - it is easy to calibrate. The main influence is the temperature. This is indicated as deviation of angle degree per degree Kelvin. Values like 0.01 deg/K look good but if you consider a +/-50 C° temperature working range, the result is an overall +/- 0.5 degree deviation. In many cases this value is indicated as ‘typical value’. Although the definition of typical varies, an example may +/- 1σ. This means that 68 % of the sensors have this characteristic.Standard_deviation_diagram.png But what about the other 32 %? Carl Friedrich Gauss gave us the answer. The maximum deviation of the sensor element is statistically valuated. | |
As you can see in the Gaussian distribution curve, 68 % is just about 1/3 of the real accuracy. Let’s say: a third of the full truth. If you cover all the sensors in this example, the real inaccuracy is considered 3 times higher. Manufactures of construction machines cannot be satisfied when they are only guaranteed that 68 % of their Machines will work within the specified range, can they? | |
This is why MOBA has established a temperature learning process. All of our temperature calibrated sensors are heated up to +85 C° and cooled down to -40°C. At these extremes they “learn” individual temperature characteristics and are able to correct themselves. This is verified with a second temperature test. If MOBA indicates the temperature accuracy, we are indicating the absolute value over the full quantity of sensors. The accuracy of the temperature-compensated MOBA Sensors is 0.002 deg/K. The whole truth. |