Data model-based sensor fault diagnosis algorithm for closed-loop control systems

doi:10.1016/j.measurement.2025.116715

Data model-based sensor fault diagnosis algorithm for closed-loop control systems

2025-03-01

0

PRE

AI

DOI：10.1016/j.measurement.2025.116715

Original

Original request for help

Save

Abstract

En 中文

The varying operational parameters and random noise make it difficult to determine the fault diagnosis thresholds for engine sensors under different working conditions. Therefore, an adaptive threshold-based fault diagnosis method for aeroengine sensors is proposed. A multivariable control system based on the MFAC method is established for the aeroengine. The OS-ELM algorithm employs historical sensor data to train and update the engine baseline model. MFAC dynamically establishes a linear model based on the pseudo-gradient change of control variables from the current sensor data and designs a baseline model tracker to calculate reasonable diagnostic thresholds based on historical sensor data characteristics, thereby improving the efficiency of threshold calculation and diagnostic accuracy. The experimental results validate that this method improves the fault detection rate by at least 30% while ensuring a low false alarm rate, reduces the minimum detectable fault magnitude by 39%, and keeps the fault detection time within 0.2 s.

Keywords:

Turboprop engine

Neural network

Adaptive threshold setting

Real-time sensor fault diagnosis

Nonlinear tracker

Data model

Data model-based sensor fault diagnosis algorithm for closed-loop control systems

Abstract

Journal

Organization