Internal combustion engine diagnostics using statistically processed Wiebe function

Abstract

The internal combustion engines diagnostics has an extensive history and a lot of diagnostic methods have been applied. The condition of the technical objects, also of the internal combustion engine, for example, on the basis of tribotechnical methods of oil degradation [20, 22, 30] can be assessed or, for example, on the basis of the assessment of the acoustic emissions [16, 17, 24], or on the basis of the assessment of vibrations [27, 29], or on the basis of the assessment of the exhaust emissions [14]. For assessing of the technical condition, of course, the statistic tools are used, as mentioned in [18]. The course of the indicated pressure depending on the crank angle allows to diagnose the condition of a lot of internal combustion engine components. An example is the Covariance method (CoV) of Indicated Mean Effective Pressure (IMEP). The method evaluates the variability of the combustion process using the IMEP parameter. The variability of the combustion process is determined by the size of the standard deviation. The method is described in [34], where it is also shown that the course of the indicated pressure depends on the amount of released heat during the fuel combustion process. The procedures, referred in an article [2], describes relationship between the course of high-pressure indication and the parameters of the Wiebe function. However, the above-mentioned approaches do not solve the specific problem of an engine diagnostics due to the variability of not only the amount of released heat (mass fraction burn), but also of the heat release rate during individual working cycles. The CoV IMEP method in principle works with the mean value of the indicated pressure, i.e. with one integral parameter, but there is no information about the distribution of the heat release rate. The presented method can thus indirectly characterize the amount of released heat, including process variability. However, the utilization of the Wiebe function also provides information about the distribution of the heat release rate. This can be advantageously utilized for the better assessment of the variability between each engine cycles, because of uses of two parameters for the burning description. This is a diagnosis of situations, such as inconsistent ignition of the mixture, or an unsuitable fuel use or local detonation combustion. The process of fuel combustion can thus be described using the Wiebe function, as shown in [4, 31], the function is computationally very efficient, as shown in [32]. However, most methods using the Wiebe function to describe fuel combustion are focused on the best possible compliance between the theoretical model represented by the parameters of the Wiebe function and the experimentally measured values [5, 8, 11, 12, 13]. For example, „double Wiebe“ models The aim of the article is to present the concept of an indirect diagnostic method using the assessment of the variability of the amount of released heat (mass fraction burn) and the heat release rate. The Wiebe function for the assessment of variability has been used. The Wiebe function parameters from the course of the high-pressure indication in the cylinder of internal combustion engine using linear regression have been calculated. From a sufficiently large number of measured samples, the upper and lower limits of the Wiebe function parameters have been statistically determined. Lower and upper limits characterize variability of the heat release process not only in terms of quantity but also in terms of heat release rate. The assessment of variability is thus more complicated than using one integral indicator, typically the mean value of amount of the released heat. The procedure enabling a more accurate estimation of heat generation beginning has been shown. For the combustion process variability assessment of the engine, statistical test of relative frequencies has been used. Highlights Abstract