scholarly journals Protocol-Based Fault Detection for Discrete Delayed Systems With Missing Measurements: The Uncertain Missing Probability Case

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 76616-76626 ◽  
Author(s):  
Weilu Chen ◽  
Jun Hu ◽  
Xiaoyang Yu ◽  
Dongyan Chen
Keyword(s):  
Fault Detection ◽  
Missing Measurements ◽  
Delayed Systems

scholarly journals H∞Fault Detection for Linear Discrete Time-Varying Descriptor Systems with Missing Measurements

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Guangfu Deng ◽  
Huihong Zhao
Keyword(s):  
Fault Detection ◽  
Discrete Time ◽  
Descriptor Systems ◽  
Necessary Condition ◽  
Time Varying ◽  
Missing Measurements ◽  
Indefinite Quadratic Form ◽  
Indefinite Quadratic ◽  
Matrix Differential ◽  
Binary Switching

This paper deals with the problem ofH∞fault detection for a class of linear discrete time-varying descriptor systems with missing measurements, and the missing measurements are described by a Bernoulli random binary switching sequence. We first translate theH∞fault detection problem into an indefinite quadratic form problem. Then, a sufficient and necessary condition on the existence of the minimum is derived. Finally, an observer-basedH∞fault detection filter is obtained such that the minimum is positive and its parameter matrices are calculated recursively by solving a matrix differential equation. A numerical example is given to demonstrate the efficiency of the proposed method.

scholarly journals Fault Detection for Network Control Systems with Multiple Communication Delays and Stochastic Missing Measurements

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jie Zhang ◽  
Ming Lyu ◽  
Hamid Reza Karimi ◽  
Jian Zuo ◽  
Yuming Bo
Keyword(s):  
Fault Detection ◽  
Control Systems ◽  
Sufficient Conditions ◽  
Network Control ◽  
Communication Delays ◽  
Missing Measurements ◽  
Fault Detection Filter ◽  
Network Control Systems ◽  
Delay Phenomenon ◽  
Detection Filter

This paper is concerned with fault detection problem for a class of network control systems (NCSs) with multiple communication delays and stochastic missing measurements. The missing measurement phenomenon occurs in a random way and the occurrence probability for each measurement output is governed by an individual random variable. Besides, the multiple communication delay phenomenon reflects that networked control systems have different communication delays when the signals are transferred via different channels. We aim to design a fault detection filter so that the overall fault detection dynamics is exponentially stable in the mean square. By constructing proper Lyapunov-Krasovskii functional, we acquire sufficient conditions to guarantee the stability of the fault detection filter for the discrete systems, and the filter parameters are also derived by solving linear matrix inequality. Finally, an illustrative example is provided to show the usefulness and effectiveness of the proposed design method.

scholarly journals Fault detection and classification in Industrial IoT in case of missing sensor data

2021 ◽  
Author(s):  
Merim Dzaferagic ◽  
Nicola Marchetti ◽  
Irene Macaluso
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Sensor Data ◽  
Generative Adversarial Networks ◽  
Missing Measurements ◽  
Tennessee Eastman Process ◽  
Fault Detection And Classification ◽  
Industrial Iot ◽  
Critical Components ◽  
The Impact

This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

scholarly journals Robust fault detection of singular LPV systems with multiple time–varying delays

2016 ◽  
Vol 26 (1) ◽  
pp. 45-61 ◽  
Author(s):  
Amir Hossein Hassanabadi ◽  
Masoud Shafiee ◽  
Vicenç Puig
Keyword(s):  
Fault Detection ◽  
Reference Model ◽  
Residual Error ◽  
Unknown Input ◽  
Multiple Time ◽  
Delayed Systems ◽  
Bounded Real Lemma ◽  
Fault Sensitivity ◽  
Robust Fault Detection ◽  
Minimum Sensitivity

Abstract In this paper, the robust fault detection problem for LPV singular delayed systems in the presence of disturbances and actuator faults is considered. For both disturbance decoupling and actuator fault detection, an unknown input observer (UIO) is proposed. The aim is to compute a residual signal which has minimum sensitivity to disturbances while having maximum sensitivity to faults. Robustness to unknown inputs is formulated in the sense of the ℋ∞-norm by means of the bounded real lemma (BRL) for LPV delayed systems. In order to formulate fault sensitivity conditions, a reference model which characterizes the ideal residual behavior in a faulty situation is considered. The residual error with respect to this reference model is computed. Then, the maximization of the residual fault effect is converted to minimization of its effect on the residual error and is addressed by using the BRL. The compromise between the unknown input effect and the fault effect on the residual is translated into a multi-objective optimization problem with some LMI constraints. In order to show the efficiency and applicability of the proposed method, a part of the Barcelona sewer system is considered.

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