Engine Fault Diagnosis Combining Model-based Residuals and Data-Driven Classifiers

Abstract

Abstract Design of fault diagnosis systems is complicated by limited training data and inaccuracies in physical-based models when designing fault classifiers. A hybrid fault diagnosis approach is proposed using model-based residuals as input to a set of data-driven fault classifiers. As a case study, sensor data from an internal combustion engine test bed is used where faults have been injected into the system and a physical-based mathematical model of the air flow through the engine is available. First, a feature selection algorithm is applied to find a minimal set of residuals that is able to separate the different fault modes. Then, two different fault classification approaches are discussed, Random Forests and one-class Support Vector Machines. A set of one-class Support Vector Machines is used to model data from each fault mode separately. The case study illustrates an advantage of using one-class classifiers, which makes it possible to detect unknown faults by identifying samples not belonging to any known fault mode.