Motion fault detection and isolation in Body Sensor Networks

Significant amount of research and development is being directed on monitoring activities of daily living of senior citizens who live alone as well as those who have certain motion disorders such as Alzheimer’s and Parkinson’s. A combination of sophisticated inertial sensing, wireless communication and signal processing technologies has made such a pervasive and remote monitoring possible. Due to the nature of the sensing and communication mechanisms, these monitoring sensors are susceptible to errors and failures. In this paper, we address the issue of identifying and isolating faulty sensors in a Body Sensor Network that is used for remote monitoring of daily living activities. We identify three different types of faults in a Body Sensor Network and propose fault isolation strategies using history-based and non-history based approaches. The contributions of this paper are: (i) faulty sensor node identification in a small number of deployed body sensors (accelerometers); and (ii) identification of a faulty sensor node using a statically or dynamically bound group of sensor nodes that is sharing similar sensor signal patterns.     

无人机飞控系统故障诊断专家系统设计

针对无人机飞控系统的特点和维护要求,本文介绍了MBIT故障诊断专家系统的设计思路和实现方法,构建了无人机飞控系统故障诊断专家系统的体系结构,深入研究了如何建立故障知识库,制定推理机制,组建综合数据库和故障解释等问题。并结合某型号给出了具体的示例。在工程实际应用过程中,可以利用本系统对无人机飞控系统进行有效维护。     

Fault tolerant control for singular systems with actuator saturation and nonlinear perturbation

In this paper, the problem of robust fault tolerant control for a class of singular systems subject to both time-varying state-dependent nonlinear perturbation and actuator saturation is investigated. A sufficient condition for the existence of a fixed-gain controller is first proposed which guarantees the regularity, impulse-free and stability of the closed-loop system under all possible faults. An optimization problem with LMI constraints is formulated to determine the largest contractively invariant ellipsoid. An adaptive fault tolerant controller is then developed to compensate for the failure effects on the system by estimating the fault and updating the design parameter matrices online. Both of these two controllers are in the form of a saturation avoidance feedback with the advantage of relatively small actuator capacities compared with the high gain counterpart. An example is included to illustrate the proposed procedures and their effectiveness.     

Fault-tolerant control for a class of nonlinear sampled-data systems via a Euler approximate observer

In this paper, a fault-tolerant control (FTC) scheme is developed for a class of nonlinear sampled-data systems. First, a Euler approximate discrete model is used to describe the plant under the sampling. Under this model, an observer-based fault estimation method is proposed. To guarantee the accuracy of both the state and fault estimation values, the conditions to make the approximate model consistent with the exact model are presented. Then, an active fault-tolerant controller, which has a constraint condition for the sampling time, is designed to make the faulty system stable. Finally, an inverted pendulum is used to show the efficiency of the proposed method.     

Super connectivity of Kronecker products of graphs

Let G₁ and G₂ be two connected graphs. The Kronecker product G₁×G₂ has vertex set V(G₁×G₂)=V(G₁)×V(G₂) and the edge set . In this paper, we show that if G is a bipartite graph with κ(G)=δ(G), then G×Kn(n?3) is super-κ.     

一种新的基于迭代学习的故障检测和估计算法

将迭代学习策略应用到故障诊断中,提出一种新的故障检测和估计算法.引入虚拟故障参数,采用基于PID的迭代学习策略调节其中的虚拟故障,使虚拟故障逼近系统中实际发生的故障,从而达到对系统故障诊断的目的.理论证明和仿真结果验证了新算法的可行性和有效性.     

Redirection based recovery for MPLS network systems

To provide a reliable backbone network, fault tolerance should be considered in the network design. For a multiprotocol label switching (MPLS) based backbone network, the fault-tolerant issue focuses on how to protect the traffic of a label switched paths (LSP) against node and link failures. In IETF, two well-known recovery mechanisms (protection switching and rerouting) have been proposed. To further enhance the fault-tolerant performance of the two recovery mechanisms, the proposed approach utilizes the failure-free LSPs to transmit the traffic of the failed LSP (the affected traffic). To avoid affecting the original traffic of each failure-free LSP, the proposed approach applies the solution of the minimum cost flow to determine the amount of affected traffic to be transmitted by each failure-free LSP. For transmitting the affected traffic along a failure-free working LSP, IP tunneling technique is used. We also propose a permission token scheme to solve the packet disorder problem. Finally, simulation experiments are performed to show the effectiveness of the proposed approach.     

Integrating independent component analysis and support vector machine for multivariate process monitoring

This study aims to develop an intelligent algorithm by integrating the independent component analysis (ICA) and support vector machine (SVM) for monitoring multivariate processes. For developing a successful SVM-based fault detector, the first step is feature extraction. In real industrial processes, process variables are rarely Gaussian distributed. Thus, this study proposes the application of ICA to extract the hidden information of a non-Gaussian process before conducting SVM. The proposed fault detector will be implemented via two simulated processes and a case study of the Tennessee Eastman process. Results demonstrate that the proposed method possesses superior fault detection when compared to conventional monitoring methods, including PCA, ICA, modified ICA, ICA–PCA and PCA–SVM.     

Full-order observer-based actuator fault detection and reduced-order observer-based fault reconstruction for a class of uncertain nonlinear systems

This paper considers observer-based actuator fault detection and reconstruction problems for uncertain nonlinear systems. Based on a kind of full-order observer which is robust to disturbances but sensitive to actuator faults, a single detection observer is constructed to produce a residual which can be used to alarm the occurrence of the actuator faults when at least one actuator fault occurs indeed. The full-order observer is adaptive one because an adaptation law which can adjust the Lipschitz constant of Lipschitz term is introduced. For this reason, the Lipschitz constant can be unknown in our design. After this, a kind of reduced-order observer is developed by choosing a special observer gain matrix. Based on the reduced-order observer, we provide a kind of unknown information estimating method which can be used to not only reconstruct the actuator faults but also estimate the disturbances of the system. In simulation, a real model of the seventh-order aircraft is used to illustrate the effectiveness of the proposed methods.     

Robust monitoring and fault reconstruction based on variational inference component analysis

Probabilistic models such as probabilistic principal component analysis (PPCA) have recently caught much attention in the process monitoring area. An important issue of the PPCA method is how to determine the dimensionality of the latent variable space. In the present paper, one of the most popular Bayesian type chemometric methods, Bayesian PCA (BPCA) is introduced for process monitoring purpose, which is based on the recent developed variational inference algorithm. In this monitoring framework, the effectiveness of each extracted latent variable can be well reflected by a hyperparameter, upon which the dimensionality of the latent variable space can be automatically determined. Meanwhile, for practical consideration, the developed BPCA-based monitoring method is robust to missing data and can also give satisfactory performance under limited data samples. Another contribution of this paper is due to the proposal of a new fault reconstruction method under the BPCA model structure. Two case studies are provided to evaluate the performance of the proposed method.