Inverse combustion force estimation based on response measurements outside the combustion chamber and signal processing

摘要

Exposure to vibration has various physiological effects on vehicle passengers. Engine is one of the main sources of vehicle vibration. The major causes of engine vibration are combustion forces transmitted through the pistons and connection rods. Evaluation of sources is the first step to attenuate this vibration. Assessment of these sources is not an easy task because internal parts of machinery are not accessible. Often, instrumentation for such systems is costly, time consuming and some modifications would be necessary. Aim of the first part of this paper was to validate an inverse technique and carry out mobility analysis on a vehicle crankshaft to achieve matrix of Frequency Response Functions (FRFs). Outcomes were implemented to reconstruct the applied force for single and multiple-input systems. In the second part, the validated inverse technique and FRFs were used to estimate piston forces of an operating engine. Bearings of crankshaft were chosen as nearest accessible parts to piston connecting rods. Accelerometers were connected to the bearings for response measurement during an ideal engine operation. These responses together with FRFs, which were estimated in the previous part, were utilised in the inverse technique. Tikhonov regularization was used to solve the ill-conditioned inverse system. Two methods, namely L-curve criterion and Generalized Cross Validation (GCV), were employed to find the regularization parameter for the Tikhonov method. The inverse problem was solved and piston forces applied to crankpins were estimated. Results were validated by pressure measurement inside a cylinder and estimating the corresponding combustion force. This validation showed that inverse technique and measurement outcomes were roughly in agreement. In presence of various noise, L-curve criterion conduces to more robust results compared to the GCV method. But in the absence of high correlation between sources (f>600HzHz), the GCV technique leads to more accurate results. This research shows that inverse techniques have great ability to estimate vibration sources inside the machinery.