Ma Jian, Lu Chen, Liu Hongmei
School of Reliability and Systems Engineering, Beihang University, Beijing, China; Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing, China.
School of Reliability and Systems Engineering, Beihang University, Beijing, China.
PLoS One. 2015 Mar 30;10(3):e0122829. doi: 10.1371/journal.pone.0122829. eCollection 2015.
The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.
飞机环境控制系统（ECS）是飞机的关键系统，它提供适宜的环境条件以确保航空乘客和设备的安全运输。近年来，ECS的功能和可靠性受到越来越多的关注。热交换器是ECS中一个特别重要的部件，因为其故障会降低系统效率，进而可能导致灾难性后果。对热交换器进行故障诊断对于预防风险很有必要。然而，有两个问题阻碍了热交换器故障诊断在实际中的实施。第一，热交换器的实际测量参数不能有效地反映故障的发生，而热交换器故障通常是通过利用相应的与故障相关的状态参数来描述的，这些参数无法直接测量。第二，传统的扩展卡尔曼滤波器（EKF）和基于EKF的双模型滤波器都有一定的缺点，比如对建模误差敏感以及初始化值选择困难。为了解决上述问题，本文分别提出了一种基于强跟踪滤波器（STF）的与故障相关的参数自适应估计方法和用于热交换器故障检测及故障模式分类的改进贝叶斯分类算法。进行了热交换器故障仿真以生成故障数据，并据此对所提方法进行验证。结果表明，所提方法能够对热交换器进行准确、稳定且快速的故障诊断。
