In the past decades, with the rapid development of modern technologies, a special kind of fault, the intermittent fault (IF) has become a serious threat to system securities. Although fruitful research results have been obtained for fault diagnosis, most of them are concerned with permanent faults. Furthermore, up to now, there has been no overview on the diagnosis techniques for IFs in dynamic systems. In fact, IFs are common in a variety of fields, whereas their research is far from mature and many open issues still remain to be solved. Therefore, in this overview, our main objective is to attract direct attentions from more researchers and engineers into the fascinating area of IF diagnosis. In this paper, the concept, research background, challenges and research motivations of IFs are introduced. Then, current research into diagnostics of IFs is reviewed, along with discussions on their strengths, limitations, and future directions.

Although the partial least squares (PLS) approach is an effective fault detection method, some issues of nonlinear process monitoring related to key performance indicators (KPIs) still exist. To address the nonlinear characteristics in the industrial processes, kernel partial least squares (KPLS) method has been proposed. However, the KPLS method also faces some difficulties in fault detection. None of the existing KPLS methods can accurately decompose measurements into KPI-related and KPI-unrelated parts, and these methods usually ignore the fact that the residual subspace still contains some KPI-related information. In this study, a new improved KPLS method, which considers the KPI-related information in the residual subspace, has been proposed for KPI-related process monitoring. First, the proposed method performs general singular value decomposition (GSVD) on the calculable loadings based on the kernel matrix. Next, the kernel matrix can be suitably divided into KPI-related and KPI-unrelated subspaces. In addition, we present the design of two statistics for process monitoring as well as a detailed algorithm performance analysis for kernel methods. Finally, a numerical case and Tennessee Eastman (TE) benchmark process demonstrate the efficacy and merits of the improved KPLS-based method.

This paper is concerned with the moving horizon estimation problem for a class of discrete time-delay systems under the Round-Robin (RR) protocol. The communication between the sensor nodes and the remote state estimator is implemented via a shared network, where only one sensor node is permitted to transmit data at each time instant for the purpose of preventing data collisions. The RR protocol is utilized to orchestrate the transmission order of sensor nodes, under which the selected node obtaining access to the network could be modeled by a periodic function. A lifting technology is introduced to reformulate the system model into a linear system without delays. The aim of the addressed problem is to develop a moving horizon estimator such that the estimation error is ultimately bounded. A sufficient condition is established to ensure the ultimate boundedness in terms of a matrix inequality. Within the established theoretical framework, two optimization problems are proposed to calculate the corresponding estimator parameters according to two different performance requirements (e.g., the smallest ultimate bound and the fastest decay rate). Finally, simulation examples are given to illustrate the effectiveness of the estimator design scheme.

Fault-tolerant cooperative control of multiagent systems has attracted ever-increasing attention in recent years due to the fact that multiple agents can provide much more redundancy than a single agent system, thereby making the fault tolerant cooperative control design more flexible. However, multiagent systems may bring severe challenges that do not exist in single-agent systems. This article aims at presenting a survey of trends and methodologies of fault tolerant cooperative control in multiagent systems. Depending on the countermeasure against the faults, the existing fault-tolerant cooperative control methodologies are first classified into four categories: Individual methodologies, cooperative methodologies, topology reconfiguration-based methodologies, and composition reconfiguration-based methodologies. Then the characteristics and implementation schemes of four categories of methodologies are discussed in detail. Furthermore, the applicability of fault tolerant cooperative control in smart grids is outlined. Finally, several challenging issues are envisioned for future research.

This paper is concerned with the distributed state estimation problem over wireless sensor networks. The communication links are unreliable that are subject to random link failures modeled as a set of independent Bernoulli processes. To estimate the plant state collaboratively, a Kalman-consensus filtering approach is developed where the sensors spread the local information obtained from the Kalman filtering algorithm by performing a consensus of the inverse covariance matrices at each time instant. Sufficient conditions for the stochastic boundedness of the Kalman-consensus filter are established. It is shown that the filtering performance is directly influenced by the network connectivity and the collective observability. A numerical example is illustrated to verify the proposed results.

This article is concerned with the moving horizon estimation (MHE) problem for networked linear systems (NLSs) with unknown inputs under dynamic quantization effects. For the NLSs with unknown input signals, the conventional MHE strategy is incapable of guaranteeing the satisfactory performance as the estimation error is dependent on the external disturbances. In this work, a novel MHE strategy is developed to cope with the underlying NLS with unknown inputs by dedicatedly introducing certain temporary estimates of unknown inputs, where the desired estimator parameters are designed to decouple the estimation error dynamics from the unknown inputs. A two-step design strategy (namely, decoupling step and convergence step) is proposed to obtain the estimator parameters. In the decoupling step, the decoupling parameter of the moving horizon estimator is designed based on certain assumptions on system parameters and quantization parameters. In the convergence step, by employing a special observability decomposition scheme, the convergence parameters of the moving horizon estimator are achieved such that the estimation error dynamics is ultimately bounded. Moreover, the developed MHE strategy is extended to the scenario with direct feedthrough of unknown inputs. Two simulation examples are given to demonstrate the correctness and effectiveness of the proposed MHE strategies.