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model-based detection of soft faults using the smoothed residual for a control system

MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第1期32卷 015107-015107页

作者： Liu, Jinxin Yang, Liangdong Xu, Maojun Zhang, Qian Yan, Ruqiang Chen, Xuefeng Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710054 Peoples R China

Soft faults are a very common failure mode in electro hydraulic servo control systems in aero engines, including component characteristic drift, performance degradation and so on. Traditional fault detection approaches for soft faults haveproblems of time delay, inaccuracy and false alarms. In order to solve these problems, a model-based detection approach for soft faults is proposed in this paper. Firstly, a robust, time-saving and reliable modeling method is proposed. This method utilizes a mechanism model and a data-driven correction model to characterize the main features and uncertain features of the practical system, respectively. The data-driven correction model is implemented by a backpropagation neural network, which adopts the input of the actual system and the output of the mechanism model as its training data, and adopts the output of the actual system as its training label. This neural correction model plays an important role in compensating for the deviation between the mechanism model and the practical system. Secondly, a robust fault observer, which is based on the residual between the measurement output of the practical system and the model output of the hybrid mechanism-neural network model, is designed to detect the soft fault. The squared residual is smoothed by a median exponential filter in order to eliminate the disturbances of impulsive interference or noises in measurement. Finally, the detection of soft faults is implemented by comparing the smoothed residual with a presetting threshold. Several simulations have been performed to verify the effectiveness of the proposed method, and the simulation results prove the advancement of the proposed method.

关键词： soft fault model-based detection neural network smoothed residual

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model-based detection of Cyber-Attacks in Networked MPC-based Control Systems

Model-based Detection of Cyber-Attacks in Networked MPC-base...

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10th International-Federation-of-Automatic-Control (IFAC) Symposium on Fault detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Barboni, Angelo Boem, Francesca Parisini, Thomas Imperial Coll London Dept Elect & Elect Engn London England Univ Cyprus KIOS Res & Innovat Ctr Excellence Nicosia Cyprus Univ Trieste Dept Engn & Architecture Trieste Italy UCL Dept Elect & Elect Engn London England

In this preliminary work, we consider the problem of detecting cyber-attacks in a linear system equipped with a model Predictive Controller, where the feedback loop is closed over a non-ideal network, and the process is subject to a random Gaussian disturbance. We adopt a model-based approach in order to detect anomalies, formalizing the problem as a binary hypothesis test. The proposed approach exploits the analytical redundancy obtained by computing partially overlapping nominal system trajectories over a temporal sliding window, and propagating the disturbance distributions along them. The recorded data over such window is then used to define a probabilistic consistency index at each time step in order to make a decision about the presence of possible attacks. Preliminary simulation results show the effectiveness of the proposed attack-detection method. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Networked control Cyber-attacks model-based detection probabilistic prediction methods uncertain linear systems

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model-based detection of Structural Anomalies: An On-Line Monitoring Approach

Model-Based Detection of Structural Anomalies: An On-Line Mo...

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Oceans (Conference)

作者： J. V. Candy S. N. Franco E. L. Ruggiero M. C. Emmons I. M. Lopez L. M. Stoops R. A. Fellini Lawrence Livermore National Laboratory

ISBN: (纸本)9781509064298;9780692946909

Mechanical systems operating in noisy environments create a challenging signal processing and monitoring problem especially in real-time. To detect a particular type of subsystem from noisy vibration data, it is necessary to identify signatures or particular features that make it unique. Resonant (modal) frequencies emitted during its normal operation satisfy this constraint. Monitoring structural modes to determine the condition of a system under investigation is essential, especially if it is a critical entity of an operational system. The development of a model-based detection scheme capable of the on-line detection of structural anomalies applying both system identification methods to extract a modal model and state estimation/detection methods for anomaly detection is developed. Two optimal detectors evolve: an innovations-based processor and a reference detection scheme.

关键词： model-based detection Structural reference

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Command Injection Attacks in Smart Grids: A Survey

IEEE OPEN JOURNAL OF INDUSTRY APPLICATIONS

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IEEE OPEN JOURNAL OF INDUSTRY APPLICATIONS 2024年 5卷 75-85页

作者： Usama, Muhammad Aman, Muhammad Naveed Univ Nebraska Sch Comp Lincoln NE 68588 USA

Cybersecurity is important in the realization of various smart grid technologies. Several studies have been conducted to discuss different types of cyberattacks and provide their countermeasures. The false command injection attack (FCIA) is considered one of the most critical attacks that have been studied. Various techniques have been proposed in the literature to detect FCIAs on different components of smart grids. The predominant focus of current surveys lies on FCIAs and detection techniques for such attacks. This article presents a survey of existing works on FCIAs and classifies FCIAs in smart grids according to the targeted component. The impacts of FCIAs on smart grids are also discussed. Subsequently, this article provides an extensive review of detection studies, categorizing them based on the type of detection technique employed.

关键词： Artificial intelligence (AI)-based detection algorithms command injection cyber-physical systems (CPSs) detection techniques model-based detection smart grid

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Optimization of Process-Aware Attack detection for Industrial Control Systems Security 25

Optimization of Process-Aware Attack Detection for Industria...

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25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Sicard, Franck Hotellier, Estelle Perez-Olivares, Javier Soto Zamai, Eric Naval Grp Naval Cyber Lab Ollioules France INSA Lyon AMPERE Lab MSE Dept Villeurbanne France

ISBN: (纸本)9781728189567

Industrial Control Systems (ICS) are present in many fields, sometimes critical infrastructures, and perform complex functions to carry out their missions. However, since the beginning of the century, they have been the target of increasingly specific attacks whose consequences can be catastrophic. Thus, new security approaches at the border between IT security and safety have emerged to detect malicious activities in ICS. S.A.F.E. (Security Approach based on Filter Execution) is one of them as it enables the order analysis issued by the Programmable Logic Controller (PLC) based on behavioral models. Indicators are used to trigger automation-oriented detection mechanisms. One of these indicators, the distance, measures the minimal number of orders from the PLC to apply from the current state before reaching a prohibited state. This computation is based on the Dijkstra algorithm which is time consuming and complex. This paper presents a comparative study of different algorithms for optimizing distance computation in a cybersecurity context.

关键词： model-based detection Graph Theory Anomaly detection Cybersecurity Control System Security Cyber-Physical Systems

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Is your commute driving you crazy? A study of misbehavior in vehicular platoons 15

Is your commute driving you crazy? A study of misbehavior in...

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8th ACM Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2015

作者： DeBruhl, Bruce Weerakkody, Sean Sinopoli, Bruno Tague, Patrick Carnegie Mellon University United States

ISBN: (纸本)9781450336239

Traffic is not only a source of frustration but also a leading cause of death for people under 35 years of age. Recent research has focused on how driver assistance technologies can be used to mitigate traffic fatalities and create more enjoyable commutes. In this work, we consider cooperative adaptive cruise control (CACC) or platooning, a driver assistance technology that controls the speed of vehicles and inter-vehicle spacing. CACC equipped cars use radar to fine tune inter-vehicle spacing and dedicated short-range communication (DSRC) to collaboratively accelerate and decelerate. Platooning can reduce fuel consumption by over 5% and increases the density of cars on a highway. Previous work on platooning has focused on proving string stability, which guarantees that the error between cars does not grow with the length of a platoon, but little work has considered the impact an attacker can have on a platoon. To design safe distributed controllers and networks it is essential to understand the possible attacks that could be mounted against platoons. In this work, we design a set of insider attacks and abnormal behaviors that occur in a platoon of cars. For example, we introduce the collision induction attack where an attacker exploits the platoon controller to cause a high-speed accident with the car following it. To mitigate these insider attacks we design a model-based detection scheme that leverages the broadcast nature of DSRC. Each car uses DSRC messages from other cars in the platoon to model the expected behavior of the car directly preceding it. If the expected behavior and actual behavior differ the monitoring vehicle switches to non-cooperative ACC, relying solely on radar, to mitigate the impact of the attack. We show that our detection scheme is able to detect many of our proposed insider attacks and when combined with a well designed ACC controller can avoid collisions. We propose combining our detection scheme with a global reputation scheme to detec

关键词： model-based detection vehicle platoons vehicular network security

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Fault detection of an Industrial Heat-Exchanger: A model-based Approach

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2011年第6期57卷 477-484页

作者： Dragan, Dejan Univ Maribor Fac Logist Celje 3000 Slovenia

One of the key issues in modelling for fault detection is how to accommodate the level of detail of the model description to suit the diagnostic requirements. The paper addresses a two-stage modelling concept to an industrial heat exchanger, which is located in a tyre factory modelling relies on both, prior knowledge and recorded data. During the identification procedure, the estimates of continuous model parameters are calculated by the least squares method and the state variable filters (SVF). It is shown that the estimates are largely invariant of the bandwidth of the SVFs. This greatly reduces the overall modelling effort and makes the whole concept applicable even for less experienced users. The main issues of the modelling procedure are emphasized. based on the process model, a simple detection system is derived. An excerpt of the results obtained on operating records is given. (C) 2010 Journal of Mechanical Engineering. All rights reserved.

关键词： industrial heat exchanger fault detection condition monitoring model-based detection modelling identification

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Practical Issues of Leaks Diagnosis in Pipelines

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IFAC Proceedings Volumes 2011年第1期44卷 12337-12342页

作者： C Verde L Molina R Carrera Universidad Nacional Autnoma de Mxico Mexico Instituto Mexicano del Petrleo Mexico

Abstract The paper deals with practical issues on adjusting and implementation of leaks diagnosis systems taking into account standards of the American Petroleum Industry. Considering a pilot pipeline developed specially for leak detection tests which is 132 m long with 10.4 cm of diameter and it is instrumented with different kinds of flow and pressure sensors at the ends of the line, we share our experience about how to cope with practical aspects of the design and adjusting of multi-leaks decision systems including open issues. The study also shows the results of three detection methods tested with the same pipeline: a model-based observer, an identification approach and a data driven method.

关键词： Fault diagnosis leaks in a pilot pipeline decision conditions model-based detection data driven detection practical issues

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model-based detection of lung lesions in CT exams

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International Congress Series 2003年第C期1256卷 990-997页

作者： Kaucic, Robert A McCulloch, Colin C Mendonça, Paulo R.S Walter, Deborah J Avila, Ricardo S Mundy, Joseph L GE Global Research Niskayuna NY 12309 United States

The thorough detection of nodules in high-resolution CT lung scans is an increasingly difficult, labor-intensive, and critical radiological task. Recent clinical research on early lung cancer CT presentation has demonstrated the significant clinical need to detect the more subtle subsolid nodules as well as the traditional solid nodule. We have developed a model-based computer-aided detection (CAD) algorithm designed to automatically detect both of these nodule presentation types through the use of precise mathematical models that capture scanner physics and anatomy and pathology domain knowledge. Our model-based CAD algorithm utilizes a Bayesian framework for determining the probability of multiple competing anatomical and pathological events throughout the lung. Using this model-based CAD algorithm on 50 low-dose CT lung cancer screening cases, we measured a 3.9% average improvement in radiologist sensitivity (93.8% to 97.7%) with 8.3 false positives per case for all nodules ≥5 mm in size. This model-based approach can be easily extended to support additional anatomy and pathology models as clinical understanding and scanning practices improve. © 2003, Elsevier Science B.V. All rights reserved.

关键词： CAD CT Lung cancer screening model-based detection

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Robust STAP detection in a dense signal airborne radar environment

引用

SIGNAL PROCESSING 2004年第9期84卷 1625-1636页

作者： Michels, JH Himed, B Rangaswamy, M USAF Res Lab AFRL SNRT Rome NY 13441 USA

This paper presents the performance of several space-time adaptive processing (STAP) detection methods in a dense signal environment. These include the normalized parametric adaptive matched filter (N-PAMF), the joint domain localized (JDL), and a variant of JDL referred to as the normalized JDL (NJDL). Issues considered here include robust detection with respect to signal contamination of training data and efficient estimation procedures with limited training data. The paper also introduces the innovations power sorting (IPS) pre-processing procedure for representative training data selection. Performance analyses are carried out with measured data from the Multichannel Airborne Radar Measurement (MCARM) program. (C) 2004 Elsevier B.V. All rights reserved.

关键词： robust STAP outliers JDL NJDL PAMF N-PAMF dense signal problem reduced training data selection innovations power sorting model-based detection

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