Multisensor data fusion for automotive engine fault diagnosis

Abstract

This paper describes mainly a decision-level data fusion technique for fault diagnosis for electronically controlled engines. Experiments on a SANTANA AJR engine show that the data fusion method provides good engine fault diagnosis. In data fusion methods, the data level fusion has small data preprocessing loads and high accuracy, but requires commensurate sensor data and has poor operational performance. The decision-level fusion based on Dempster-Shafer evidence theory can process noncommensurate data and has robust operational performance, reduces ambiguity, increases confidence, and improves system reliability, but has low fusion accuracy and high data preprocessing cost. The feature-level fusion provides good compromise between the above two methods, which becomes gradually mature. In addition, acquiring raw data is a precondition to perform data fusion, so the system for signal acquisition and processing for an automotive engine test is also designed by the virtual instrument technology.