A Robust Data-Driven Fault Diagnosis scheme based on Recursive Dempster-Shafer Combination Rule *

TL;DR

This paper introduces a novel recursive Dempster-Shafer-based fault diagnosis method that adaptively weights residuals using streaming data reliability, improving online sensor fault detection accuracy.

Contribution

It proposes a new evidence-based combination rule for residual errors that enhances robustness and reduces false alarms in online sensor fault diagnosis.

Findings

Effective in reducing false alarms

Validated with multi-flight aircraft data

Outperforms existing recursive combination rules

Abstract

In-flight sensor fault diagnosis and recursive combination of residual signals via the Dempster-Shafer (DS) theory have been considered in this study. In particular, a novel evidence-based combination rule of residual errors as a function of a reliability measure derived from streaming data is proposed for the purpose of online robust sensors fault diagnosis. The proposed information fusion mechanism is divided into three steps. In the first step, the classic DS probability mass combination rule is applied; then, the difference between the previous posterior mass and the current prior mass associated with fault events is computed. Finally, the increment of the posterior mass of a fault event is weighted as a function of a reliability coefficient that depends on the norm of control activity. A Sensor Fault Isolation scheme based on the proposed combination rule has been worked out and compared with well-known state-of-the-art recursive combination rules. A quantitative analysis has been performed exploiting multi-flight data of a P92 Tecnam aircraft. The proposed approach showed to be effective, particularly in reducing the false alarms rate.