Non-Convex Logarithm Embedding Subspace Weighted Graph Approach to Fault Detection with Missing Measurements

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.

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

Mathematical Problems in Engineering ◽

10.1155/2014/690461 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 1

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.

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

10.36227/techrxiv.14540061.v1 ◽

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.

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