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 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

Optimal topology for distributed fault detection of large-scale systems

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.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

Distributed fault diagnosis for process and sensor faults in a class of interconnected input-output nonlinear discrete-time systems

This paper presents a distributed fault diagnosis scheme able to deal with process and sensor faults in an integrated way for a class of interconnected input–output nonlinear uncertain discrete-time systems. A robust distributed fault detection scheme is designed, where each interconnected subsystem is monitored by its respective fault detection agent, and according to the decisions of these agents, further information regarding the type of the fault can be deduced. As it is shown, a process fault occurring in one subsystem can only be detected by its corresponding detection agent whereas a sensor fault in a subsystem can be detected by either its corresponding detection agent or the detection agent of another subsystem that is affected by the subsystem where the sensor fault occurred. This discriminating factor is exploited for the derivation of a high-level isolation scheme.Moreover, process and sensor fault detectability conditions characterising quantitatively the class of detectable faults are derived. Finally, a simulation example is used to illustrate the effectiveness of the proposed distributed fault detection scheme

Cooperative constrained control of distributed agents with nonlinear dynamics and delayed information exchange: A stabilizing receding-horizon approach

This paper addresses the problem of cooperative control of a team of distributed agents with decoupled nonlinear discrete-time dynamics, which operate in a common environment and exchange-delayed information between them. Each agent is assumed to evolve in discrete-time, based on locally computed control laws, which are computed by exchanging delayed state information with a subset of neighboring agents. The cooperative control problem is formulated in a receding-horizon framework, where the control laws depend on the local state variables (feedback action) and on delayed information gathered from cooperating neighboring agents (feedforward action). A rigorous stability analysis exploiting the input-to-state stability properties of the receding-horizon local control laws is carried out. The stability of the team of agents is then proved by utilizing small-gain theorem results

Neural Adaptive Control of a Robot Joint Using Secondary Encoders

Using industrial robots for machining applications in flexible manufacturing processes lacks a high accuracy. The main reason for the deviation is the flexibility of the gearbox. Secondary Encoders (SE) as an additional, high precision angle sensor offer a huge potential of detecting gearbox deviations. This paper aims to use SE to reduce gearbox compliances with a feed forward, adaptive neural control. The control network is trained with a second network for system identification. The presented algorithm is capable of online application and optimizes the robot accuracy in a nonlinear simulation

Identification of sensor replay attacks and physical faults for cyber-physical systems

This letter proposes a threat discrimination methodology for distinguishing between sensor replay attacks and sensor bias faults, based on the specially designed watermark integrated with adaptive estimation. For each threat type, a watermark is designed based on the changes that the threat imposes on the system. Threat discrimination conditions are rigorously investigated to characterize quantitatively the class of attacks and faults that can be discriminated by the proposed scheme. A simulation is presented to illustrate the effectiveness of our approach

Discrimination between replay attacks and sensor faults for cyber-physical systems via event-triggered communication

In this paper, a threat discrimination methodology is proposed for cyber-physical systems with event-triggered data communication, aiming to identify sensor bias faults from two possible types of threats: replay attacks and sensor bias faults. Event-triggered adaptive estimation and backward-in-time signal processing are the main techniques used. Specifically, distinct incremental systems of the event-triggered cyber-physical system resulting from the considered threat types are established for each threat type, and the difference between their inputs are found and utilized to discriminate the threats. An event-triggered adaptive estimator is then designed by using the event-triggered sampled data based on the system in the attack case, allowing to reconstruct the unknown increments in both the threat cases. The backward-in-time model of the incremental system in the replay attack case is proposed as the signal processor to process the reconstructions of the increments. Such a model can utilize the aforementioned input difference between the incremental systems such that its output has distinct quantitative properties in the attack case and in the fault case. The fault discrimination condition is rigorously investigated and characterizes quantitatively the class of distinguishable sensor bias faults. Finally, a numerical simulation is presented to illustrate the effectiveness of the proposed methodology

Optimization Based Partitioning Selection for Improved Contaminant Detection Performance

Indoor Air Quality monitoring is an essential ingredient of intelligent buildings. The release of various airborne contaminants into the buildings, compromises the health and safety of occupants. Therefore, early contaminant detection is of paramount importance for the timely activation of proper contingency plans in order to minimize the impact of contaminants on occupants health. The objective of this work is to enhance the performance of a distributed contaminant detection methodology, in terms of the minimum detectable contaminant release rates, by considering the joint problem of partitioning selection and observer gain design. Towards this direction, a detectability analysis is performed to derive appropriate conditions for the minimum guaranteed detectable contaminant release rate for specific partitioning configuration and observer gains. The derived detectability conditions are then exploited to formulate and solve an optimization problem for jointly selecting the partitioning configuration and observer gains that yield the best contaminant detection performance

Fault diagnosis for uncertain networked systems

Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: (a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; (b) the modeling of uncertainty by developing an adaptive learning scheme; (c) the uncertainty issues emerging when considering networked systems such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems such as the physical environment, the sensor level, the fault diagnosers, and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated

Battle of Water Demand Forecasting

As part of the Battle of Water Networks competition series, the Battle of Water Demand Forecasting (BWDF) was organized in the context of the 3rd Water Distribution Systems Analysis and Computing and Control in the Water Industry (WDSA-CCWI) joint conference held in Ferrara (Italy) in 2024. In line with the previous editions of the Battle of Water Networks—the main objective of which was to address a specific problem related to the design and operation of water distribution networks—the BWDF aims to compare the effectiveness of methods for the short-term forecast of urban water demand in a set of real district metered areas. During the conference, 31 teams across the world participated in the BWDF and presented their approaches. The results obtained demonstrate the importance of (1) considering integrated approaches for short-term water demand forecasting; and (2) evaluating their performance in relation to more than one metric, case study, and period