Riccardo M.G. Ferrari

Distributed Fault Detection and Isolation of Large-Scale Discrete-Time Nonlinear Systems: An Adaptive Approximation Approach

This paper deals with the problem of designing a distributed fault detection and isolation methodology for nonlinear uncertain large-scale discrete-time dynamical systems. As a divide et impera approach is used to overcome the scalability issues of a centralized implementation, the large scale system being monitored is modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a Local Fault Diagnoser is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define 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. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability and isolability of faults affecting variables shared among overlapping subsystems. Theoretical results are provided to characterize the detection and isolation capabilities of the proposed distributed scheme. Finally, simulation results are reported showing the effectiveness of the proposed methodology.

Distributed Fault Diagnosis With Overlapping Decompositions: An Adaptive Approximation Approach

This technical note deals with the problem of designing a distributed fault detection methodology for distributed (and possibly large-scale) nonlinear dynamical systems that are modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a local fault detector is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define the subsystem interconnections. The local detection 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. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability of faults affecting variables shared among different subsystems. Simulation results provide an evidence of the effectiveness of the proposed distributed fault detection scheme.

Fault Detection and Isolation of the Wind Turbine Benchmark: An Estimation-Based Approach

Abstract In this paper, a fault detection and isolation (FDI) method is developed for wind turbines based on a benchmark system model. The FDI method follows a general architecture developed in previous papers, where a fault detection estimator is used for fault detection, and a bank of fault isolation estimators are employed to determine the particular fault type/location. Each isolation estimator is designed based on a particular fault scenario under consideration. Some representative simulation results are given to show the effectiveness of the FDI method.

Distributed Fault Detection and Isolation of Continuous-Time Non-Linear Systems

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.

Distributed fault diagnosis for continuous-time nonlinear systems: The input–output case ☆

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.

A Distributed Networked Approach for Fault Detection of Large-Scale Systems

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 multirate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based resynchronization 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 time-varying 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.

Distributed fault diagnosis with overlapping decompositions and consensus filters

This paper deals with the problem of building a distributed fault detection architecture for large-scale dynamical systems that are modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem a local fault detector is built, such that it can measure the local state of its subsystem as well as receive through communication links a measure of the neighboring states. The local detection decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptively learned approximation of the interconnection with neighbouring subsystems. The use of a specially-designed consensus filter is proposed in order to improve the capability of the diagnosers to detect faults affecting variables shared among different subsystems. Simulation results give an evidence of the effectiveness of the proposed fault detection scheme and of the use of overlapping decompositions and consensus filters.

A fault detection and isolation scheme for nonlinear uncertain discrete-time sytems

This paper presents a fault detection and isolation scheme for abrupt and incipient faults in nonlinear uncertain discrete-time systems. The proposed fault diagnosis architecture consists of the fault detection and approximation estimator and a bank of fault isolation estimators, each corresponding to a particular type of fault. A time-varying threshold that guarantees no false-positive alarms and fault detectability conditions is derived analytically. For the fault isolation scheme, we design adaptive residual thresholds associated with each isolation estimator and obtain sufficient conditions for fault isolability. To illustrate the theoretical results, a simulation example based on a discrete-time version of the three-tank problem is presented.

A robust fault detection and isolation scheme for a class of uncertain input-output discrete-time nonlinear systems

This paper extends very recent results on discrete- time nonlinear fault detection and isolation to the case of discrete-time nonlinear systems with unstructured modeling uncertainty and partial state measurement. The fault diagnosis architecture consists of a fault detection and approximation estimator and a bank of fault isolation estimators, each corresponding to a particular type of fault. A time-varying threshold that guarantees no false-positive alarms and fault detectability conditions are derived analytically. For the fault isolation scheme, we design adaptive residual thresholds associated with each isolation estimator and obtain sufficient conditions for fault isolability. To illustrate the theoretical results, a simulation example based on a input-output discrete-time version of the three-tank benchmark problem is presented.

A distributed fault detection methodology for a class of large-scale uncertain input-output discrete-time nonlinear systems

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.