DeepConvolutionalTransferLearningNetworkANewMethodforIntelligentFaultDiagnosisofMachineswithUnlabeledData(深度卷积迁移学习网络：一种新的无标记机器故障智能诊断方法) 雷亚国团队2018年文章。 机械智能故障诊断的成功依赖于两个条件。1)含有故障信息的有标签数据是可用的。2)训练和测试数据是从相同的概率分布中获取的。然而，对于大部分机械，很难获得大量的有标签数据。此外，尽管一些机械可以获取有标签数据，但是由于数据分布的差异，用这些有标签数据训练的智能故障诊断方法可能无法对从其他机械获得的无标

移动计算环境的复杂性、网络条件多样性,便携设备有限的资源、弱连接性、安全漏洞等因素,使得系统中故障发生的可能性增加,有效地感知、隔离、恢复故障可提高系统容错性能。该文针对具有中心结点类的移动计算环境,运用互测比较的方法,通过小区中的移动结点和移动支持站进行交互测试来获取故障征兆信息;基于概率诊断方法对检测结果进行评估,运用不同的处理策略来诊断故障结点集。对算法性能进行了理论分析和仿真实验,结果表明:算法能够满足移动计算环境下结点故障感知要求,具有较高的可诊断性和较低的时间、通信开销。

Observer-based adaptive fault-tolerant control of a class of nonlinear systems with actuator failures

The evolvable hardware (EHW) is widely used in the design of fault-tolerant system. Fault-tolerant system is really a real-time system, and the recovery time is necessary in fault detection and recovery. However, when applying EHW, real-time characteristic is usually ignored. In this paper, a fault-tolerant strategy based on EHW is proposed. The recovery time, predicted by the fault tree analysis (FTA), is considered as a constraint condition. A configuration library is set up in the design phas

Wind energy has been drawing considerable attention in recent years. However, due to the random nature of wind and high failure rate of wind energy conversion systems (WECSs), how to implement fault-tolerant WECS control is becoming a significant issue. This paper addresses the fault-tolerant contro

The main contribution of this paper is the development of H-infinity fault tolerant control for a wind energy conversion system (WECS) based on the stochastic piecewise affine (PWA) model. In this paper the normal and fault stochastic PWA models for WECS including multiple working points at

INTERNATIONAL STANDARD CEI IEC 61025 - Fault tree analysis (FTA) by [prepared by IEC Technical Committee 56, Dependability]. (z-lib.org)

永磁同步电机内部故障分析，方红伟，，提出了一种考虑饱和现象的磁路耦合分析法，分析了永磁同步发电机的定子绕组匝间短路和转子偏心综合故障。谐波电流被用以分析一台

关于电力系统建模和故障分析的一本外文书籍。

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems 2014 杜伊斯堡埃森大学 丁教授