Robust Fault Diagnosis in the Presence of Process Uncertainties

Han, Zhengang; Li, Weihua; Shah, Sirish L.

doi:10.3182/20020721-6-es-1901.00810

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…The residuals vector R(t) depending on the inputs as an uncertain system, actuator faults cannot and outputs is obtained as be detected, even if the conditions for perfect or imperfect decoupling [17] are satisfied, because of…”

Section: Of the System Several Methods Have Been Developedmentioning

confidence: 99%

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On the fault diagnosis of uncertain singularly perturbed systems

Tellili¹,

Abdelkrim²,

Benrejeb

2006

Proceedings of the Institution of Mechanical Engineers, Part I:

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This work aims at the fault diagnosis of two-timescale, singularly perturbed systems (SPSs) and SPSs with a multimodelling structure. The approach of model-based fault detection and isolation using the dynamic parity space to generate singular perturbation parameter free residuals was used. A comparative study of diagnosis of the SPS is carried out considering, in the first case, the SPS as an uncertain system with parameter uncertainties and using, in the second case, the slow subsystem for fault detection and isolation. The results are applied to a power system.

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Section: Of the System Several Methods Have Been Developedmentioning

confidence: 99%

“…Equation ( 18) enables the condition for perfect decoupling, m−n>0, to be obtained. For localization of the faults, m−n−1>0 must be guaranteed [17]. H l1…”

Section: The Augmented Uncertainty Vector (14) Has Thementioning

confidence: 99%

On the fault diagnosis of uncertain singularly perturbed systems

Tellili¹,

Abdelkrim²,

Benrejeb

2006

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Moreover, the experimental data is typically obtained with the presence of sensors errors and measurement noise, modeling errors, and external disturbances that can impact on the system model accuracy; see, e.g., Reference [ 5 ]. This scenario has been considered for sensor or actuator failure diagnosis and isolation in systems with structural uncertainties and noisy measurements, utilizing residual analysis and Bayesian frameworks to develop failure detection procedures [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models

Orellana

Carvajal

Escárate

et al. 2021

Sensors

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In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and able to describe the entire dynamics of an aircraft. In addition, the complexity of modern real systems has turned deterministic models impractical, since they cannot adequately represent the behavior of disturbances in sensors and actuators, and tool and machine wear, to name a few. Thus, it is necessary to deal with model uncertainties in the dynamics of the plant by incorporating a stochastic behavior. These uncertainties could also affect the effectiveness of fault diagnosis methodologies used to increment the safety and reliability in real-world systems. Determining suitable dynamic system models of real processes is essential to obtain effective process control strategies and accurate fault detection and diagnosis methodologies that deliver good performance. In this paper, a maximum likelihood estimation algorithm for the uncertainty modeling in linear dynamic systems is developed utilizing a stochastic embedding approach. In this approach, system uncertainties are accounted for as a stochastic error term in a transfer function. In this paper, we model the error-model probability density function as a finite Gaussian mixture model. For the estimation of the nominal model and the probability density function of the parameters of the error-model, we develop an iterative algorithm based on the Expectation-Maximization algorithm using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.

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