In this paper, diagnosis for hybrid systems using a parity space approach that considers model uncertainty is proposed. The hybrid diagnoser is composed of modules which carry out the mode recognition and diagnosis tasks both based on residuals generated using a model. Both tasks interact each other since the diagnosis module adapts himself according to the current mode of the hybrid system. Moreover, the methodology takes into account the parameter uncertainty using a passive robust strategy. An adaptive threshold for residual evaluation is generated and the parity space approach is used to design a set of residuals for each mode. The proposed fault diagnosis approach for hybrid systems is illustrated on a piece of the Barcelona sewer network.

 This paper presents a novel methodology named Health-Aware Control (HAC) system, that integrates System Health Monitoring (SHM), Control and Prognosis. This new approach based on both current and future health state estimates, provided by the prognosis module, takes into account the systems health information in the control objectives. In this way, the control actions are generated to fulfill the control goals and, at the same time, to extend the life of the system components. To illustrate the performance of the proposed approach, a conveyor belt system that uses an AC electrical motor to move a cart from one end to the other is used.

 This article is basically focused on application of the Robust Kalman Filter (RKF) algorithm to the estimation of high speed an autonomous underwater vehicle (AUV) dynamics. In the normal operation conditions of AUV, conventional Kalman filter gives sufficiently good estimation results. However, if any kind of malfunction occurs in the system, KF gives inaccurate results and diverges by time. This study, introduces Adaptive Fading Kalman Filter (AFKF) algorithm with the filter gain correction for the case of system malfunctions. By the use of defined variables named as single and multiple fading factors, the estimations are corrected without affecting the characteristic of the accurate ones.

 This paper presents a dual layer approach for robust fault tolerant estimation of nonlinear processes using a combined adaptive extended Kalman filter and fault detection and filter reconfiguration. From the one hand, the filter is made robust in face of environment uncertainty using adaptive filtering. To this end, the filter identifies the measurement covariance by means of recursive estimation, upon which the adaptation relies, to suppress the effect of sporadic variations in the quality of measurements as well as compensates for incipient sensor faults. From the other hand, fault monitoring is continuously applied to the filter's innovation in an attempt to initiate filter reconfiguration when the adaptation mechanism alone is not able to overcome the failure situation. The discussion of the results is embedded in the application framework of state estimation of a batch distillation process.

 In this paper a network planning problem aiming to enable underground Medium Voltage (MV) power grids to resilient PowerLine Communications (PLCs) is faced. The PLC network is used to connect PLC End Nodes (ENs) located into the secondary substations to the energy management system of the utility by means of PLC network nodes enabled as Access Points. An optimization problem is formulated, aiming to optimally allocate the Access Points to the substations and the repeaters to the MV feeders. A multi-objective optimization approach is used, in order to keep in balance the needs of minimizing the cost of equipment allocation and maximizing the reliability of PLC network paths. Resiliency and capacity constraints are properly modeled, in order to guarantee the communications even under faulted link conditions. As a byproduct, the optimization algorithm also returns the optimal routing. Simulations performed on a realistic underground MV distribution grid validate the proposed approach

 We address the problem of state estimation of the power system for the Smart Grid. We assume that the monitoring of the electrical grid is done by a network of agents with both computing and communication capabilities. We propose a security mechanism aimed at protecting the state estimation process against false data injections originating from faulty equipment or cyber-attacks. Our approach is based on a multi-agent filtering scheme, where in addition to taking measurements, the agents are also computing local estimates based on their own measurements and on the estimates of the neighboring agents. We combine the multi-agent filtering scheme with a trust-based mechanism under which each agent associates a trust metric to each of its neighbors. These trust metrics are taken into account in the filtering scheme so that information transmitted from agents with low trust is disregarded. In addition, a mechanism for the trust metric update is also introduced, which ensures that agents that diverge considerably from their expected behavior have their trust values lowered.