某座舱压力调节器的故障诊断及预测研究

Research of Fault Diagnosis and Prediction for a Certain Cabin Pressure Regulator

摘要:

座舱压力调节系统由调节器、传感器及控制装置等组成，是一个结构复杂的系统。对其进行全面故障诊断与预测，有助于提升飞机可靠性与安全性，降低维护成本，改善航空运输效率与服务质量。为了提高座舱压力调节系统的性能和可靠性，本文提出了一种基于机器学习与深度学习的故障诊断方法。该方法以座舱压力调节器为核心诊断对象，构建系统级的数据采集与处理框架，提取多源传感器信号特征，并利用支持向量机（SVM）和卷积神经网络（CNN）等算法对其运行状态进行分类与预测。实验结果表明，该方法能够准确识别调节器的异常状态，显著提高故障预警能力，为航空器座舱环境控制系统的智能维护提供了技术支持。

Abstract:

The cabin pressure control system, consisting of a regulator, sensors, and control units, is a complex and critical component of modern aircraft. Comprehensive fault diagnosis and prediction of this system contribute to enhancing aircraft reliability and safety, reducing maintenance costs, and improving the efficiency and service quality of air transportation.To improve the performance and reliability of the cabin pressure control system, this paper proposes a fault diagnosis method based on machine learning and deep learning. Focusing on the cabin pressure regulator as the core diagnostic component, a system-level data acquisition and processing framework is established to extract features from multi-source sensor signals. Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) are employed to classify and predict the operational states. Experimental results show that the proposed method can accurately identify abnormal conditions of the regulator, significantly enhancing fault prediction capability and providing technical support for intelligent maintenance of the aircraft cabin environmental control system.