Jung, Daniel (Linköping University), Säfdal, Joakim (Linköping University)

A Flexi-Pipe Model for Residual-Based Engine Fault Diagnosis to Handle Incomplete Data and Class Overlapping

Scheduled for presentation during the Regular Session "Modeling, Estimation, and Control of Internal Combustion Engine- II" (MoBT3), Monday, August 29, 2022, 15:30−15:50, Pfahl Hall 140

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on April 24, 2024

Abstract

Data-driven fault diagnosis of dynamic systems is complicated by incomplete training data, unknown faults, and overlapping classes. Many existing machine learning models and data-driven classifiers are not expected to perform well if training data is not representative of all relevant fault realizations. In this work, a data-driven model, called a flexi-pipe model, is proposed to capture the variability of data in residual space from a few realizations of each fault class. A diagnosis system is developed as an open set classification algorithm that can handle both incomplete training data and overlapping fault classes. Data from different fault scenarios in an engine test bench is used to evaluate the performance of the proposed methods. Results show that the proposed fault class models generalize to new fault realizations when training data only contains a few realizations of each fault class.