Work

Fault Diagnosis of Aero‐Engine Gas Path System Under Time‐varying Operating Condition

Year: 2025

Type: article

Abstract: ABSTRACT An aero‐engine usually works in time‐varying operating condition. The coupling effect between gas path fault mode and operating condition makes the characteristics of fault mode vary with the operating condition. It increases the difficulty of fault diagnosis of gas path system. Moreover, class imbalance commonly exists between normal data and fault mode data. To address these challenges, we propose a multi‐stage semi‐supervised fault diagnosis method for gas path system, considering time‐varying operating condition and class imbalance. At the first stage, a multilayer perceptron (MLP) is constructed to identify the operating condition based on altitude, Mach number, fuel flow, and high and low‐pressure rotor speed. At the second stage, an auxiliary classifier Wasserstein generative adversarial network with gradient penalty (ACWGAN‐GP) model is developed to create fault samples during the self‐training process. All the data are divided into three categories, that is, pressure, speed, and temperature data, which are used to train fault diagnosis mode, respectively. The Dempster–Shafer evidence theory is employed for the information fusion at the decision level. The effectiveness of our proposed method is validated by data generated by a gas turbine simulation program.

Source: Quality and Reliability Engineering International

DOI: 10.1002/qre.70006

Authors: Shijie Zhang, Jiawen Hu, Lei Wang

Institutions: University of Electronic Science and Technology of China, Southwest Jiaotong University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Natural Science Foundation of Sichuan Province

Award: G6087295612