Local Fault Detection And Isolation Research Articles

Modeling and fault diagnosis design for HVAC systems using recurrent neural networks

In this work, we develop models and a fault detection and isolation (FDI) methodology for heating, ventilation and air conditioning (HVAC) systems that utilizes recurrent neural networks (RNN). The FDI design does not require the existence of plant fault history, mechanistic models or a set of expert rules to isolate faults. The key is to first use plant data to build predictive models and input/output estimators, and then embed them within FDI filters. A distributed FDI framework is designed consisting of local FDI (LFDI) schemes that communicate with each other for improved FDI. The distributed FDI framework enables diagnosis of multiple faults in different components of the HVAC system when a fault in one of the control components directly affects the other subsystems. The effectiveness of the proposed FDI scheme is shown via simulation examples on a simulation test bed, as well as using real data. The simulations revealed superior performance of the proposed FDI methodology over FDI approaches using subspace based models for both simulation and real data cases.

Distributed fault diagnosis of heating, ventilation, and air conditioning systems

The problem of distributed fault detection and isolation (FDI) for heating, ventilation, and air conditioning (HVAC) systems has been addressed in this work. First, a linear model is identified for subunits of an HVAC system. Next, a local FDI (LFDI) framework is designed for each unit under consideration. A distributed FDI architecture is designed where the LFDI frameworks communicate to exchange information to achieve enhanced FDI in each unit. As a result, each LFDI framework functions as intended even in the presence of faults that affect multiple units. Effectiveness of the proposed distributed FDI framework is shown for various commonly occurring fault scenarios. © 2018 American Institute of Chemical Engineers AIChE J, 65: 640–651, 2019

Distributed fault diagnosis for networked nonlinear uncertain systems

In this work, we address the problem of simultaneous fault diagnosis in nonlinear uncertain networked systems utilizing a distributed fault detection and isolation (FDI) strategy. The key idea is to design a bank of local FDI (LFDI) schemes that communicate with each other for improved FDI. The proposed distributed FDI scheme is shown to be able to handle local faults as well as those that affect more than one subsystem. This is achieved via appropriate adaptation of the LFDI filters based on information exchange with other subsystems and using the proposed notion of detectability index. The detectability index and isolability conditions are rigorously derived for the distributed FDI scheme. Effectiveness of the proposed methodology is shown via application to a reactor-separator process subject to uncertainty and measurement noise.

Distributed Fault Detection and Isolation for Interconnected Systems: a Non-Asymptotic Kernel-Based Approach

In this paper, a novel framework is proposed for deadbeat distributed Fault Detection and Isolation (FDI) of large-scale continuous-time LTI dynamic systems. The monitored system is composed of several subsystems which are linearly interconnected with unknown parameterization. Each subsystem is monitored by a local diagnoser based on the measured local output, local inputs and the interconnection variables from the neighboring subsystems. The local FDI decision is based on two non-asymptotic state-parameter estimators using Volterra integral operators which eliminate the effect of the unknown initial conditions so that the estimates converge to the true value in a deadbeat manner and therefore the fault diagnosis can be achieved in finite time. Moreover, the unknown interconnection parameters and the unknown fault parameters are simultaneously estimated. Numerical examples are included to show the effectiveness of the proposed FDI architecture.

Distributed fault detection and isolation resilient to network model uncertainties.

The ability to maintain state awareness in the face of unexpected and unmodeled errors and threats is a defining feature of a resilient control system. Therefore, in this paper, we study the problem of distributed fault detection and isolation (FDI) in large networked systems with uncertain system models. The linear networked system is composed of interconnected subsystems and may be represented as a graph. The subsystems are represented by nodes, while the edges correspond to the interconnections between subsystems. Considering faults that may occur on the interconnections and subsystems, as our first contribution, we propose a distributed scheme to jointly detect and isolate faults occurring in nodes and edges of the system. As our second contribution, we analyze the behavior of the proposed scheme under model uncertainties caused by the addition or removal of edges. Additionally, we propose a novel distributed FDI scheme based on local models and measurements that is resilient to changes outside of the local subsystem and achieves FDI. Our third contribution addresses the complexity reduction of the distributed FDI method, by characterizing the minimum amount of model information and measurements needed to achieve FDI and by reducing the number of monitoring nodes. The proposed methods can be fused to design a scalable and resilient distributed FDI architecture that achieves local FDI despite unknown changes outside the local subsystem. The proposed approach is illustrated by numerical experiments on the IEEE 118-bus power network benchmark.

Distributed sensor fault detection and isolation for multimachine power systems

SUMMARYThis paper presents a distributed sensor fault detection and isolation (FDI) scheme for multimachine power systems. Each generator is interconnected with other generators through a transmission network, where the interactions between directly interconnected generators are nonlinear. In the distributed FDI scheme, a local FDI component is designed for each generator excitation system in the power system based on local measurements and certain communicated information from other FDI components associated with generators that are directly interconnected to the local generator. In each FDI component, adaptive thresholds for distributed FDI are derived, ensuring robustness with respect to nonlinear interconnection and unstructured modeling uncertainty under certain conditions. Furthermore, the fault detectability and isolability properties are investigated, characterizing the class of sensor faults that are detectable and isolable by the distributed FDI method. In addition, the stability and learning capability of the local adaptive fault isolation estimators designed for each generator is derived. A simulation example of a two‐machine power system is used to illustrate the effectiveness of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.

Distributed sensor fault diagnosis in a class of interconnected nonlinear uncertain systems

In this paper, a distributed sensor fault detection and isolation (FDI) method is developed for a class of interconnected nonlinear uncertain systems. In the distributed FDI architecture, a FDI component is designed for each subsystem in the interconnected system. For each subsystem, its corresponding local FDI component is designed by utilizing local measurements and certain communicated information from neighboring FDI components associated with subsystems that are directly interconnected to the particular subsystem under consideration. Under certain assumptions, adaptive thresholds for distributed sensor fault detection and isolation in each subsystem are derived, ensuring robustness with respect to interactions among subsystems and system modeling uncertainty. Moreover, the fault detectability condition is rigorously investigated, characterizing the class of sensor faults in each subsystem that is detectable by the proposed distributed FDI method. Additionally, the stability and learning capability of the distributed adaptive fault isolation estimators is established. A simulation example of interconnected inverted pendulums mounted on carts is used to illustrate the effectiveness of the distributed FDI method.

Distributed fault diagnosis in a class of interconnected nonlinear uncertain systems

In this article, a distributed fault detection and isolation (FDI) method is developed for a class of interconnected nonlinear uncertain systems. In the distributed FDI architecture, a FDI component is designed for each subsystem in the interconnected system. For each subsystem, its corresponding local FDI component is designed by utilising local measurements and certain communicated information from neighbouring FDI components associated with subsystems that are directly interconnected to the particular subsystem under consideration. Under certain assumptions, adaptive thresholds for distributed FDI in each subsystem are derived, ensuring robustness with respect to interactions among subsystems and system modelling uncertainty. Moreover, the fault detectability and isolability conditions are rigorously investigated, characterising the class of faults in each subsystem that are detectable and isolable by the proposed distributed FDI method. Additionally, the stability and learning capability of the local adaptive fault isolation estimators designed for each subsystem is established. A simulation example of interconnected inverted pendulums mounted on carts is used to illustrate the effectiveness of the method.

Distributed Sensor Fault Diagnosis in a Class of Interconnected Nonlinear Uncertain Systems

In this paper, a distributed sensor fault detection and isolation (FDI) method is developed for a class of interconnected nonlinear uncertain systems. In the distributed FDI architecture, a FDI component is designed for each subsystem in the interconnected system. For each subsystem, its corresponding local FDI component is designed by utilizing local measurements and certain communicated information from neighboring FDI components associated with subsystems that are directly interconnected to the particular subsystem under consideration. Under certain assumptions, adaptive thresholds for distributed sensor fault detection and isolation in each subsystem are derived, ensuring robustness with respect to interactions among subsystems and system modeling uncertainty. A simulation example of interconnected inverted pendulums mounted on carts is used to illustrate the effectiveness of the method.

Distributed Fault Detection and Isolation in a Class of Large-Scale Nonlinear Uncertain Systems

AbstractThis paper presents a distributed fault detection and isolation (FDI) method for a class of large-scale nonlinear uncertain systems. In the distributed FDI architecture, a local FDI component is designed for each subsystem by utilizing local measurements and certain communicated information from neighboring FDI components associated with subsystems that are directly interconnected. Under certain assumptions, adaptive thresholds for distributed FDI in each local subsystem are derived, ensuring robustness with respect to interactions among subsystems and system modeling uncertainty. The fault isolability condition is rigorously investigated, characterizing the class of faults that are isolable by the proposed method. An application example of automated highway systems is used to illustrate the effectiveness of the method.

An application of decentralized estimation in a fault detection problem

This paper presents a design of a decentralized fault detection and isolation (FDI) filter by means of an overlapping decentralized estimation algorithm based on a consensus strategy. An efficient solution to the FDI problem can be obtained by an adequate system decomposition into overlapping subsystems and the construction of local FDI estimators aimed at achieving the desired performance. The general aspects and properties of a consensus based estimator are described in the first part of the paper. An applicability of such an estimator to an FDI problem in a large scale system is discussed next. Namely, a case study related to the detection of fire dissymmetry in a thermal power plant boiler is presented, including the process description and identification procedure, comparison between the results obtained by local and decentralized estimators and conclusions concerning their validity.

Robust FDI for FTC Coordination in a Distributed Network System

This paper focuses on the development of a suitable Fault Detection and Isolation (FDI) strategy for application to a system of inter-connected and distributed systems, as a basis for a fault-tolerant Network Control System (NCS) problem. The work follows a recent study showing that a hierarchical decentralized control system architecture may be suitable for fault-tolerant control (FTC) of a network of distributed and interacting subsystems. The main idea is to use robust FDI methods to facilitate the discrimination between faults acting within one subsystem and faults acting in other areas of the network, so that a powerful form of active FTC of the NCS can be implemented, through an autonomous network coordinator. By using a robust form of the Unknown Input Observer (UIO), fault effects in each subsystem are de-coupled from the other subsystems, thus facilitating a powerful way to achieve local FDI in each subsystem under autonomous system coordination. Whilst the autonomous distributed control system provides active FTC under learning control, the FDI-based Reconfiguration Task enhances the network fault-tolerance, so that more significant subsystem faults can be accommodated in order to achieve a suitable standard of Quality of Performance (QoP) of the NCS.