Investigation of rotational velocity sensors

University essay from Linköpings universitet/Institutionen för systemteknik


To improve the speed measurement of construction equipment, different sensor technologies have been investigated. Many of these sensor technologies are very interesting but to keep the extent of the thesis only two was chosen for testing, magnetic absolute angle sensors using Hall and GMR technology, to investigate if those are a valid replacement for the current measurement system that is using a passive sensor. Tests show that these sensors are capable of speed measurement, but because of noisy angle estimates they need filtering for good speed computation. This filtering introduces a large time delay that is of significance for the quality of the estimate. A Kalman filter has been implemented in an attempt to lower the time delays but since only a very simple model has been used it does not give any improvements over ordinary low pass filtering. For these sensors the mounting tolerance is of great interest. For best performance the offset between the sensor and magnet centres need to be kept small for both sensors. This is due to a non-linearity effect this causes. The distance between the sensors and the magnet is not critical for linearity issues, but only for the quality of the signal, where it might drop out when the distance is too large. This is where the sensor using GMR technology stands out. Compared to the Hall technology sensor, the GMR sensor can handle distances that are more than 10 times larger. The conclusion is that these sensors can be a valid replacement of the current measurement system. They will introduce more functionality with the capability of detecting rotational direction and zero velocity. In an application with more than one sensor they can also be used for more purposes, like detecting slip in clutches etc. Depending on the application, the time delays may not be critical, else more work need to be done to improve the estimate, e.g. with a more advanced model for the Kalman filter.


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