Methods of high-dimensional statistical analysis for the prediction and monitoring of engine oil quality

Sammanfattning: Engine oils fill important functions in the operation of modern internal combustion engines. Many essential functions are provided by compounds that are either sacrificial or susceptible to degradation. The engine oil will eventually fail to provide these functions with possibly unrepairable damages as a result. To decide how often the oil should be changed, there are several laboratory tests to monitor the oil condition, e.g. FTIR (oxidation, nitration, soot, water), viscosity, TAN (acidity), TBN (alkalinity), ICP (elemental analysis) and GC (fuel dilution). These oil tests are however often labor intensive and costly and it would be desirable to supplement and/or replace some of them with simpler and faster methods. One way, is to utilise the whole spectrum of the FTIR-measurements already performed. FTIR is traditionally used to monitor chemical properties at specific wave lengths, but also provides information, in a more multivariate way though, relevant for viscosity, TAN, and TBN. In order to make use of the whole FTIR-spectrum, methods capable of handling high dimensional data have to be used. Partial Least Squares Regression (PLSR) will be used in order to predict the relevant chemical properties. This survey also considers feature selection methods based on the second order statistic Higher Criticism as well as Hierarchical Clustering. The Feature Selection methods are used in order to ease further research on how infrared data may be put into usage as a tool for more automated oil analyses. Results show that PLSR may be utilised to provide reliable estimates of mentioned chemical quantities. In addition may mentioned feature selection methods be applied without losing prediction power. The feature selection methods considered may also aid analysis of the engine oil itself and feature work on how to utilise infrared properties in the analysis of engine oil in other situations.