Combining model-based and learning-based anomaly detection schemes for increased performance and safety of aircraft braking controllers

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI:10.1016/j.engappai.2024.109551
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Abstract

In aircraft, the braking system is a safety-critical and heavily used component of the landing gear, prone to significant wear. Anomalies arising in the wear dynamics can degrade the performance of the braking system and compromise the safety of ground handling maneuvers. In this work, we tackle the problem of detecting incipient anomalies in aircraft brakes in a tightly coupled implementation with the Brake Control Unit (BCU). Two complementary approaches are presented. The first one is an observer-based architecture designed on the longitudinal aircraft dynamics that returns physically interpretable outputs connected to the wear process and allows us to improve braking performance online. The second one is an end-to-end convolutional autoencoder-based architecture that returns an anomaly score computed on data collected by the BCU with inherent robustness to modeling uncertainty, which the model-based one does not. A combined architecture that allows one to exploit the features of both model-based and learning-based approaches is proposed, which shows its capability of optimally blending the two. The approaches are evaluated in a MATLAB/Simulink multibody simulation environment that is able to replicate the braking actuator wear dynamics, demonstrating remarkable performances in anomaly detection, anti-skid control performance, and safety improvement.
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结合基于模型和基于学习的异常检测方案,提高飞机制动控制器的性能和安全性
在飞机上,制动系统是起落架上一个对安全至关重要且使用频繁的部件,容易发生严重磨损。磨损动态中出现的异常会降低制动系统的性能,并危及地面操纵的安全性。在这项工作中,我们以与制动控制单元(BCU)紧密耦合的方式,解决了飞机制动器中初期异常情况的检测问题。本文提出了两种互补方法。第一种是基于纵向飞机动力学设计的观察器架构,可返回与磨损过程相关的物理可解释输出,使我们能够在线改进制动性能。第二种是基于卷积自动编码器的端到端架构,该架构根据 BCU 收集的数据计算异常得分,对模型不确定性具有固有的鲁棒性,而基于模型的架构则不具备这种鲁棒性。我们还提出了一种组合架构,可同时利用基于模型的方法和基于学习的方法的特点,从而显示出将这两种方法进行优化组合的能力。这些方法在 MATLAB/Simulink 多体仿真环境中进行了评估,该环境能够复制制动器磨损动态,在异常检测、防滑控制性能和安全性改进方面表现出色。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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