Simultaneous-Fault Diagnosis of Satellite Power System Based on Fuzzy Neighborhood ζ-Decision-Theoretic Rough Set

Due to the increasing complexity of the entire satellite system and the deteriorating orbital environment, multiple independent single faults may occur simultaneously in the satellite power system. However, two stumbling blocks hinder the effective diagnosis of simultaneous-fault, namely, the difficulty of obtaining the simultaneous-fault data and the extremely complicated mapping of the simultaneous-fault modes to the sensor data. To tackle the challenges, a fault diagnosis strategy based on a novel rough set model is proposed. Specifically, a novel rough set model named FNζDTRS by introducing a concise loss function matrix and fuzzy neighborhood relationship is proposed to accurately mine and characterize the relationship between fault and data. Furthermore, an attribute rule-based fault matching strategy is designed without using simultaneous-fault data as training samples. The numerical experiments demonstrate the effectiveness of the FNζDTRS model, and the diagnosis experiments performed on a satellite power system illustrate the superiority of the proposed approach.

由于整个卫星系统日益复杂以及轨道环境不断恶化，卫星电力系统中可能会同时出现多个独立的单一故障。然而，有两个障碍阻碍了对并发故障的有效诊断，即获取并发故障数据困难以及并发故障模式到传感器数据的映射极其复杂。为了应对这些挑战，提出了一种基于新型粗糙集模型的故障诊断策略。具体而言，通过引入简洁的损失函数矩阵和模糊邻域关系，提出了一种名为FNζDTRS的新型粗糙集模型，以准确挖掘和表征故障与数据之间的关系。此外，设计了一种基于属性规则的故障匹配策略，该策略不使用并发故障数据作为训练样本。数值实验证明了FNζDTRS模型的有效性，在卫星电力系统上进行的诊断实验表明了所提方法的优越性。