Data-Driven Engine Fault Classification and Severity Estimation Using Interpolated Fault Modes from Limited Training Data

• Main Author: Säfdal, Joakim
• Format: Others
• Language: English
• Published: Linköpings universitet, Fordonssystem 2021
• Subjects: Vehicle Engineering; Farkostteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173916

Today modern vehicles are expected to be safe, environmentally friendly, durable and economical. Monitoring the health of the vehicle is therefore more important than ever. As the complexity of vehicular systems increases the need for efficient monitoring methods has increased as well. Traditional methods of deriving models for the systems are today not as efficient as the complexity of the systems increases the time and skill needed to implement the models. An alternative is data driven methods where a collection of data associated with the behavior of the system is used to draw conclusions of the state of the system. Faults are however rare events and collecting sufficient data to cover all possible faults threatening a vehicle would be impossible. A method for drawing conclusions from limited historical data would therefore be desirable. In this thesis an algorithm using distiguishability as a method for fault classification and fault severity estimation is proposed. Historical data is interpolated over a fault severity vector using Gaussian process regression as a way to estimate fault modes for unknown fault sizes. The algorithm is then tested against validation data to evaluate the ability to detect and identify known fault classes and fault serveries, separate unknown fault classes from known fault classes, and estimate unknown fault sizes. The purpose of the study is to evaluate the possibility to use limited historical data to reduce the need for costly and time consuming data collection. The study shows promising results as fault class identification and fault size estimation using the proposed algorithm seem possible for fault sizes not included in the historical data.