用于卫星异常检测的Real NVP归一化流模型的故障注入分析
Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection
AI总结：
本研究分析用于卫星故障检测的物理感知Real NVP归一化流模型对单粒子翻转的resilience，提出TensorFlow故障注入框架，通过层状态和输出注入不同故障类型，揭示关键位翻转的影响，为容错措施提供指导。
摘要：
卫星被用于多种应用，包括通信、地球观测和空间科学。神经网络和基于深度学习的方法目前代表了提升这些任务性能和效率的最先进技术。鉴于卫星易受各种故障影响，人工智能（AI）的一个关键应用是故障检测。然而，尽管神经网络具有优势，这些系统容易受到辐射误差的影响，这会显著影响其可靠性。确保这些解决方案的可靠性需要广泛的测试和验证，特别是使用故障注入方法。本研究分析了一种物理感知（PI）实值非体积保持（Real NVP）归一化流模型在空间系统故障检测中的应用，重点关注其对单粒子翻转（SEUs）的 resilience。我们提出了一个基于TensorFlow的定制故障注入框架来评估神经网络的resilience。故障注入通过两种主要方法进行：层状态注入，针对权重和偏置等内部网络组件；以及层输出注入，修改不同激活函数下的层输出。故障类型包括零值、随机值和位翻转操作，应用于不同级别和不同网络层。我们的发现揭示了几个关键见解，例如关键位中的位翻转错误的重要性，这可能导致性能大幅下降甚至系统故障。通过这项工作，我们旨在全面研究Real NVP模型对辐射引起的误差的resilience，为指导容错措施的实施提供一种手段。
英文摘要：
Satellites are used for a multitude of applications, including communications, Earth observation, and space science. Neural networks and deep learning-based approaches now represent the state-of-the-art to enhance the performance and efficiency of these tasks. Given that satellites are susceptible to various faults, one critical application of Artificial Intelligence (AI) is fault detection. However, despite the advantages of neural networks, these systems are vulnerable to radiation errors, which can significantly impact their reliability. Ensuring the dependability of these solutions requires extensive testing and validation, particularly using fault injection methods. This study analyses a physics-informed (PI) real-valued non-volume preserving (Real NVP) normalizing flow model for fault detection in space systems, with a focus on resilience to Single-Event Upsets (SEUs). We present a customized fault injection framework in TensorFlow to assess neural network resilience. Fault injections are applied through two primary methods: Layer State injection, targeting internal network components such as weights and biases, and Layer Output injection, which modifies layer outputs across various activations. Fault types include zeros, random values, and bit-flip operations, applied at varying levels and across different network layers. Our findings reveal several critical insights, such as the significance of bit-flip errors in critical bits, that can lead to substantial performance degradation or even system failure. With this work, we aim to exhaustively study the resilience of Real NVP models against errors due to radiation, providing a means to guide the implementation of fault tolerance measures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
