Fault Detection and Isolation Based on Structural Analysis: Application to a Multi-Engine Propulsion Cluster

TL;DR

This paper develops a fault detection and isolation system for multi-engine propulsion clusters using structural analysis and sensitivity indices.

Contribution

A novel FDI system is proposed using Structural Analysis and new sensitivity indices (SSI and RSI) for fault isolation in multi-engine clusters.

Findings

Over 16,000 residual generator candidates were obtained from the model of a three-engine cluster.

The proposed Subset Sensitivity Index (SSI) and Residual Sensitivity Index (RSI) improve fault isolation accuracy.

Monte Carlo simulations validated the FDI system's performance under ten different faults.

Abstract

A multi-engine cluster of a launcher operates in extreme conditions, making the tasks of detection and isolation complex, with only a precise group of faults with small magnitudes worth being detected and isolated. To address this, a model-based Fault Detection and Isolation (FDI) system is developed for a multi-engine cluster using Structural Analysis (SA). A model of a multi-engine cluster composed of three engines is established, where different sensor and actuator faults are considered. After analyzing the model using SA, more than 16,000 residual generator candidates are obtained. An algorithm is then proposed to find subsets of residuals with minimal cardinality capable of isolating all faults. Each subset generates a unique fault signature for each fault, enabling fault isolation. The selection of the best subsets is based on the new definition of the Subset Sensitivity Index (SSI), according to which the subsets composed of the most sensitive residuals have the highest SSI values. The residual sensitivity is calculated using a new method based on the signal-to-noise ratio (SNR), where a new definition of the Residual Sensitivity Index (RSI) is given. Furthermore, a new algorithm is presented for fault isolation. Suppose a theoretical fault signature is not observed. In that case, the algorithm uses the knowledge of the residual sensitivity (RSI) to define which residual did not behave as expected and predicts which fault is active in the system. Monte Carlo simulations are conducted under ten different faults to analyze the performance of the proposed FDI system, including actuators and sensor faults.

Figures