scholarly journals LFT Bond Graph for Online Robust Fault Detection and Isolation of Hybrid Multi-Source System

2021 ◽  
Vol 2065 (1) ◽  
pp. 012010
Author(s):  
Mahdi Boukerdja ◽  
Youness Radi ◽  
Omprakash ◽  
Sumit Sood ◽  
Belkacem Ould-Bouamama ◽  
...  
Keyword(s):  
Fault Detection ◽  
Online Monitoring ◽  
Detection Algorithm ◽  
Bond Graph ◽  
Fault Detection And Isolation ◽  
False Alarms ◽  
Parameter Uncertainties ◽  
Safety Issues ◽  
Robust Fault Detection ◽  
Production Of Hydrogen

Abstract Green hydrogen is undoubtedly the most promising energy vector of the future because it is captured by renewable and inexhaustible sources, such as wind and/or solar energy, and can be stored over the long in high-pressure cylinders, which can be used to feed the fuel cells to produce the electricity without emitting any pollutants. The system incorporated renewable sources and process used to produce the green hydrogen is the hybrid multi-source system (HMS). The production of hydrogen needs a reliable HMS, which always requires online monitoring for real-time Fault Detection and Isolation (FDI) because the risk of accidents in HMS and safety issues increases due to the possibility of faults. However, online monitoring of FDI is challenging due to the multi-physics dynamics of HMS and the inclusion of uncertain parameters and several disturbances. This paper proposes an online robust fault detection algorithm to detect system faults based on the properties of the graphical linear fractional transformation bond graph (LFT-BG) modeling approach. Here, the analytical redundancy relations (ARRs) and their uncertain parts extracted from the LFT-BG model are used to develop an online robust FDI algorithm for HMS. Numerical evaluations of ARRs and their uncertain parts, respectively, generate the residual signals known as “faults indicators” and their uncertain bounds known as “adaptive thresholds.” These thresholds evolve with system variables in the presence of parameter uncertainties for ensuring robust FDI for HMS to minimize false alarms. The validation of this approach is carried out using 20sim software that is familiar with BG modeling.

scholarly journals Dynamic Thresholds for a Reliable Diagnosis of Switched Systems

2021 ◽  
Vol 54 (6) ◽  
pp. 827-833
Author(s):  
Ayman Abboudi ◽  
Fouad Belmajdoub
Keyword(s):  
Fault Detection ◽  
Switched Systems ◽  
Bond Graph ◽  
Fault Detection And Isolation ◽  
False Alarms ◽  
Main Concern ◽  
Model Errors ◽  
Reliable Diagnosis ◽  
No Sensor ◽  
Fault Detectors

Safety, availability and reliability are the main concern of many industries. Thus, fault detection and isolation of industrial machines, which are in most cases switched systems, is a primary task in many companies. The presented paper proposes a new diagnostic approach for switched systems using two powerful tools: bond graph and observer. A diagnostic layer detects model errors using bond graph, and a smart algorithm identifies and locates faults using observer. Although observers serve as fault detectors, they also have their own errors caused by convergence delay of calculations; even in the case of no sensor defect, the residue does not converge to zero. In this paper, we propose a new method to solve this problem by integrating dynamic thresholds in the detection procedure, which helped to avoid false alarms and ensure a highly reliable diagnosis.

Optimized fault detection using bond graph in linear fractional transformation form

2021 ◽  
pp. 095965182098561
Author(s):  
Mahdi Ouziala ◽  
Youcef Touati ◽  
Sofiane Berrezouane ◽  
Djamel Benazzouz ◽  
Belkacem Ouldbouamama
Keyword(s):  
Fault Detection ◽  
Detection System ◽  
Main Idea ◽  
Bond Graph ◽  
Fault Detection And Isolation ◽  
Linear Fractional Transformation ◽  
Estimated Parameters ◽  
Robust Fault Detection ◽  
Uncertainty Calculation ◽  
Adaptive Thresholds

This article deals with the optimal robust fault detection problem using the bond graph in its linear fractional transformation form. Generally, this form of the bond graph allows the generation of two perfectly separate analytical redundancy relations, that are used as residual and threshold. However, the uncertainty calculation method gives overestimated thresholds. This may, for instance, lead to undetectable faults. Therefore, enhancing the robustness of fault detection and isolation algorithms is of utmost importance in designing a bond graph–based fault detection system. The main idea of this article is to develop optimized thresholds to ensure an optimal detection, otherwise this article proposes a method to detect tiny magnitude faults concerning parameter’s uncertainties. This work considers the issue of optimal fault detection as an optimization problem of the gap between the residuals and its threshold. New uncertainty values will be calculated in a way that these estimated parameters ensure the desired optimized gap between residuals and thresholds. These estimated uncertainty values will be used to generate optimized adaptive thresholds. Through these thresholds, we increase the sensitivity of the residuals to tiny magnitude faults, and we ensure an optimal and early detection.

Sign in / Sign up

Export Citation Format

Share Document