Hybrid Bond Graph for robust diagnosis to measurement uncertainties

Rahal, Mohamed Ilyas; Bouamama, Belkacem Ould; Meghebbar, Abdelmajid

doi:10.1109/icosc.2016.7507079

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…In the field of BG models, these methods are still useful to handle the measurement uncertainty. In a previous study [23], modulated sources were added to the diagnostic hybrid BG (DHBG) to represent the measurement uncertainty at the signal junction and global analytical redundancy relations (GARRs) induced from the modified model can distinguish between the mode change and measurement uncertainty. A few literatures have considered the parameter uncertainty [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Improved bond graph model for fault diagnosis under uncertain conditions

Chen

Fan

Zhong

et al. 2023

Meas. Sci. Technol.

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The effectiveness of model-based diagnosis is strongly dependent on the authenticity of the model and is influenced by various uncertainties. Measurement uncertainty is governed by the probability method and parameter uncertainty can be handled by the linear fractional transformation (LFT) while structure uncertainty and errors are rarely considered. In this paper, an improved bond graph (BG) model is proposed, which adopts subsystems to substitute modelling errors, namely parameter uncertainty and structure uncertainty. Multi-dimensional Fibonacci optimization algorithm (MFO) is developed to identify parameters in subsystems to obtain subsystem-based diagnostic hybrid bond graph (SDHBG) model. Fault diagnosis is realized by comparing the residuals and thresholds derived from the SDHBG. Experiments are conducted to validate the key concepts of the proposed methods and the results suggest the effectiveness.

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Section: Introductionmentioning

confidence: 99%

Improved bond graph model for fault diagnosis under uncertain conditions

Chen

Fan

Zhong

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Optimal Adaptive Threshold and Mode Fault Detection for Model-Based Fault Diagnosis of Hybrid Dynamical Systems

Prakash

Samantaray

Bhattacharyya

2018

Fault Diagnosis of Hybrid Dynamic and Complex Systems

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Enhanced stochastic coding framework for discriminating faults and cyber incidents in hybrid systems

Badr,

Talebi,

Khosravi

2024

IET Control Theory & Appl

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This article presents an innovative approach for distinguishing replay attacks from faults in hybrid systems. To develop the idea at first, a novel technique for replay attack detection, which differentiates it from typical system faults, is introduced. This is achieved through a tactical combination of stochastic coding and a window‐based comparison mechanism, setting a new standard in hybrid system security. In the realm of fault detection, robust L2‐L∞ adaptive observers are employed for precise mode detection, allowing for an accurate assessment of the system's operational state. Additionally, robust H∞ Sliding Mode Observers are utilized to identify abnormal behaviours in the system's output, further solidifying the fault detection capabilities. A significant enhancement in the proposed approach is the integration of a Luenberger observer with the Butterworth low‐pass filter. This novel addition not only refines the filtering process but also contributes to the overall reliability and accuracy of the system. The efficiency and versatility of these methods are demonstrated through their application to a four‐tank hybrid system. This practical simulation showcases the adaptability of the approach to real‐world scenarios, highlighting its potential in diverse applications within the field of hybrid systems.

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Hybrid Bond Graph for robust diagnosis to measurement uncertainties

Cited by 7 publications

References 12 publications

Improved bond graph model for fault diagnosis under uncertain conditions

Improved bond graph model for fault diagnosis under uncertain conditions

Optimal Adaptive Threshold and Mode Fault Detection for Model-Based Fault Diagnosis of Hybrid Dynamical Systems

Enhanced stochastic coding framework for discriminating faults and cyber incidents in hybrid systems

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