Symbolic Time-Series Analysis of Engine Combustion Measurements

Abstract

We present techniques of symbolic time-series analysis which are useful for analyzing temporal patterns in dynamic measurements of engine combustion variables. We focus primarily on techniques that characterize predictability and the occurrence of repeating temporal patterns. These methods can be applied to standard, cycle-resolved engine combustion measurements, such as IMEP and heat release. The techniques are especially useful in cases with high levels of measurement and/or dynamic noise. We illustrate their application to experimental data from a production V8 engine and a laboratory single-cylinder engine. MOTIVATION Recent studies have demonstrated that cyclic combustion variations in spark-ignition engines under lean fueling exhibit patterns that can be explained as the result of noisy nonlinear combustion instabilities [1, 2, 3, 4]. These instabilities are dominated by the effects of residual cylinder gas (prior-cycle effects) and noisy perturbations of engine parameters. Because this dynamical noise obscures the underlying deterministic patterns, it is difficult to observe changes in these patterns as nominal engine parameter values are changed. This difficulty is often increased by inaccuracies in measurement of characteristic combustion parameters such as IMEP and heat release.

Characterizing dilute combustion instabilities in a multi-cylinder spark-ignited engine using symbolic analysis

“…This approach was most useful for detecting non-random (i.e. temporally related) variations in individual cylinders [5]. The second partitioning approach we utilized was based on dividing the range of IMEP values in each time series into a fixed number of equal size bins (equi-spaced bins).…”

“…Based on this, we hypothesize that the large oscillations are promoted by the presence or absence of unburned fuel in the EGR loop, which is transported back to the intake and then either stimulates or suppresses combustion in each cylinder depending on how close the charge mixture dilution is relative to the extinction limit. Symbolic analysis has previously been used to characterize discrete time series obtained from internal combustion engine combustion processes [3,5,10]. Symbolic analysis was chosen because of its ability to select specific dynamical patterns in time series with significant levels of noise or high-dimensional components [11].…”

“…stochastic) processes associated with the in-cylinder physics, such as fluid mixing, valve flutter and spark dynamics also perturbing the dynamics. Symbolization of the cycle-to-cycle combustion intensity provides a useful tool for observing these emergent features amid the stochastic background [3,5]. The presence of deterministic features is important, because it can provide some degree of predictability and, thus, the possibility of dynamic cycle-by-cycle controls capable of suppressing combustion oscillations and extension of the practical lean operating limit.…”

Symbol Statistics for Concept Formation in AI Agents

“…Work in [8] dealt with finding good models of dynamical systems by trying to match the symbol statistics of the experimental data with that of a model. A practical application of the approach on a real experimental system (an internal combustion engine) is presented in [10]. This work used a hypothesis testing technique called Shuffled Surrogates to provide confidence limits on the symbol statistics analysis, and we adopt this technique here to identify true concepts from our set of candidate concepts.…”

An Independence Test Based on Symbolic Time Series

“…As mentioned by Finney et al [38] the concept of symbolization has its roots in dynamical-systems theory, particularly in the study of nonlinear systems which can exhibit bifurcation and chaos. Besides the computational efficiency, symbolic methods are also robust when noise is present.…”