Francesca Boem

Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique.

Abstract In this paper, some new results on distributed fault diagnosis of continuous-time nonlinear systems with partial state measurements are proposed. By exploiting an overlapping decomposition framework, the dynamics of a nonlinear uncertain large-scale dynamical system is described as the interconnections of several subsystems. Each subsystem is monitored by a Local Fault Diagnoser: a set of local estimators, based on the nominal local dynamic model and on an adaptive approximation of the interconnection and of the fault function, allows to derive a local fault decision. A consensus-based protocol is used in order to improve the detectability and the isolability of faults affecting variables shared among different subsystems because of the overlapping decomposition. A sufficient condition ensuring the convergence of the estimation errors is derived. Finally, possibly non-conservative time-varying threshold functions guaranteeing no false-positive alarms and theoretical results dealing with detectability and isolability sufficient conditions are presented.

This paper presents a continuous-time distributed fault detection and isolation methodology for nonlinear uncertain possibly large-scale dynamical systems. The monitored system is modeled as the interconnection of several subsystems and a divide et impera approach using an overlapping decomposition is adopted. Each subsystem is monitored by a Local Fault Diagnoser using the information based on the measured local state of the subsystem as well as the measurements about neighboring states thanks to the subsystem interconnections. The local diagnostic decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. In order to improve the detectability and isolability of faults affecting variables shared among different subsystems, a consensus-based estimator is designed. Theoretical results are provided to characterize the detection and isolation capabilities of the proposed distributed scheme.

Abstract The paper deals with the problem of defining the optimal topology for a distributed fault detection architecture for non-linear large-scale systems. A stochastic modelbased framework for diagnosis is formulated. The system structural graph is decomposed into subsystems and each subsystem is monitored by one local diagnoser. It is shown that overlapping of subsystems allows to improve the detectability properties of the monitoring architecture. Based on this theoretical result, an optimal decomposition design method is proposed, able to define the minimum number of detection units needed to guarantee the detectability of certain faults while minimizing the communication costs subject to some computation cost constraints. An algorithmic procedure is presented to solve the proposed optimal decomposition problem. Preliminary simulation results show the potential of the proposed approach.

This paper deals with the problem of modelling a chemical reactor for the Direct Reduction of Iron ore (DRI). Such a process is being increasingly promoted as a more viable alternative to the classic Blast Furnace for the production of iron from raw minerals. Due to the inherent complexity of the process and the reactor itself, its effective monitoring and control requires advanced mathematical models containing distributed-parameter components. While classical approaches such as Finite Element or Finite Differences are still reasonable options, for accuracy and computational efficiency reasons, an algebraic approach is proposed. A full multi-physical, albeit one-dimensional model is addressed and its accuracy is analysed.

This paper proposes a delay compensation strategy for a distributed fault detection architecture, allowing to manage delays and packet losses in the communication network between the Local Fault Diagnosers. A novel consensus-based estimator with time-varying weights is introduced, permitting to improve detectability in the case of variables shared among more than one subsystem. In the consensus protocol, at each step each agent uses only the information given by the agent and the communication link which are more reliable at that time. The convergence of the proposed estimator is demonstrated and analytical conditions for detectability are derived.

In this paper, new results on distributed fault diagnosis of continuous–time nonlinear systems with partial state measurements are proposed. Following an overlapping decomposition framework, the dynamics of a nonlinear uncertain large-scale dynamical systems is described as the interconnection of several subsystems. Each subsystem is monitored by its own Local Fault Diagnoser, based on a set of local estimators. A consensus-based protocol is used to improve the detectability and the isolability of faults affecting variables shared among different subsystems because of the overlapping decomposition. A sufficient condition assuring the convergence of the estimation errors is derived. Time-varying threshold functions guaranteeing no false-positive alarms and theoretical results containing detectability and isolability conditions

This paper extends very recent results on a distributed fault diagnosis methodology for nonlinear uncertain large-scale discrete-time dynamical systems to the case of partial state measurement. The large scale system being monitored is modeled, following a divide et impera approach, as the interconnection of several subsystems that are allowed to overlap sharing some state components. Each subsystem has its own Local Fault Diagnoser: the local detection is based on the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. A consensus-based estimator is used in order to improve the detectability of faults affecting variables shared among different subsystems. Time-varying threshold functions guaranteeing no false-positive alarms and analytical fault detectability sufficient conditions are presented as well.