Independent component analysis for detection of condition changes in large diesels

Automatic detection and classification of operation conditions in large diesel engines is of significant importance. This paper investigates an independent component analysis (ICA) framework for unsupervised detection of changes in and possibly classification of operation conditions such as lubrication changes and increased wear based on acoustical emission (AE) sensor signals. The probabilistic formulation of ICA enables a statistical detection of novel events which do not conform to the current ICA model, thus indicating significant changes in operation conditions. Novelty of an observation is measured through the likelihood that the model has produced that observation. Evaluation of likelihood ratios allows the framework to also handle multiple models, thus enabling classification of operation conditions; furthermore the likelihood also serves as a link to traditional change detection. The framework is evaluated on measured AE signals in an experiment where the operational condition varies. In particular, we compare the performance of mean field ICA, information-maximization ICA, and Principal Component Analysis. For detection of changes the performance is also compared to standard methods, e.g. mean value step detection.