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Virtual sensing, i.e., the method of estimating quantities of interest indirectly via measurements of other quantities, has received a lot of attention in various fields: Virtual sensors have successfully been deployed in intelligent building systems, the process industry, water quality control, and combustion process monitoring. In most of these scenarios, measuring the quantities of interest is either impossible or difficult, or requires extensive modifications of the equipment under consideration – which in turn is associated with additional costs. At the same time, comprehensive data about equipment operation is collected by ever increasing deployment of inexpensive sensors that measure easily accessible quantities. Using this data to infer values of quantities which themselves are impossible to measure – i.e., virtual sensing – enables monitoring and control applications that would not be possible otherwise.

Future developments for small engines, e.g. engines for handheld working tools, like chain saws require the integration of ECU-systems for engine control. For small engines often only a rotational speed senor is available. The application of additional engine sensors is in many cases unwanted, e.g. due to cost aspects and additional wiring. The lack of sensor data requires tailored control strategies and signal processing techniques to infer information about the engine from the sensor data. E.g. for rotational speed sensors the Δω method has been proposed, where the load is estimated from the temporal variation of the rotational speed. This approach requires a rotational speed sensor with sufficient angular resolution. In this paper we present a simulation study for a sensor fusion concept to improve the temporal resolution of engine speed measurements for low cost engines by means of an additional vibration sensor.

Small engines for non-automotive and two wheeler applications have a reduced number of sensors. For fulfilling emission regulations a cost effective way is an enhanced use of standard sensors in order to obtain more information from the existing sensors. The delivered information can then be used for an on-board diagnosis. Moreover, it is important to control the quality of the product during engine production; therefore an end-of-line cold engine test is often made. With this measure it is possible to detect faults, wrong tolerances or assembly in order not to deliver faulty engines to the customers. In this paper, an enhanced use of sensors for fault detection will be discussed. It is possible to obtain more information from the signal or to use the sensor for detecting other parameters. For extracting information signal analysis methods will be used with focus on the computational power need since the ECU performance is limited.