The fault detection approach using bond graph model

2017 ◽  
Author(s):  
Maroua Said ◽  
Nadia Hajji ◽  
Okba Taouali ◽  
Kais Bouzrara
Keyword(s):  
Fault Detection ◽  
Graph Model ◽  
Bond Graph ◽  
Detection Approach

Bond graph model-based fault detection using residual sinks

2008 ◽  
Vol 223 (3) ◽  
pp. 337-352 ◽  
Author(s):  
W Borutzky
Keyword(s):  
Fault Detection ◽  
Numerical Computation ◽  
Graph Model ◽  
Numerical Differentiation ◽  
Equation System ◽  
Bond Graph ◽  
Engineering System ◽  
Differential Algebraic Equation ◽  
Symbolic Form ◽  
Model Based

In this paper, residual sinks are used in bond graph model-based quantitative fault detection for the coupling of a model of a faultless process engineering system to a bond graph model of the faulty system. By this way, integral causality can be used as the preferred computational causality in both models. There is no need for numerical differentiation. Furthermore, unknown variables do not need to be eliminated from power continuity equations in order to obtain analytical redundancy relations (ARRs) in symbolic form. Residuals indicating faults are computed numerically as components of a descriptor vector of a differential algebraic equation system derived from the coupled bond graphs. The presented bond graph approach especially aims at models with non-linearities that make it cumbersome or even impossible to derive ARRs from model equations by elimination of unknown variables. For illustration, the approach is applied to a non-controlled as well as to a controlled hydraulic two-tank system. Finally, it is shown that not only the numerical computation of residuals but also the simultaneous numerical computation of their sensitivities with respect to a parameter can be supported by bond graph modelling.

scholarly journals Bond Graph Model for Fault Detection of Partial Shaded PV Array Considering Different Module Connection Schemes and Effects of Bypass Diodes

2019 ◽  
Vol 01 (01) ◽  
pp. 41-59 ◽  
Author(s):  
Abd Essalam BADOUD
Keyword(s):  
Fault Detection ◽  
Fault Location ◽  
Graph Model ◽  
Bond Graph ◽  
Optimal Placement ◽  
Health State ◽  
Pv System ◽  
Future Health ◽  
Pv Array ◽  
Detection And Diagnosis

Fault detection in solar photovoltaic (PV) arrays is a fundamental task to protect PV modules from damage and to eliminate risks of safety hazards. In this work, we show a new methodology for automatic supervision and fault detection of PV Systems, based mainly on optimal placement of sensors. This supposes the possibility to build a dynamic model of the system by using the bond graph tool, and the existence of a degradation model in order to predict its future health state. The choice of bond graph is motivated by the fact that it is well suited for modeling physical systems where several types of energies are involved. Fault behavior of PV arrays is highly related to the fault location, fault impedance, irradiance level, and use of blocking diodes. In this work, PV array is connected using series parallel (SP) and Total Cross Tied (TCT) configurations including sensors to measure voltage and currents. The simulation results show the importance of the approach applied for the detection and diagnosis of fault in PV system. These results have been contrasted with real measured data from a measurement campaign plant carried on electrical engineering laboratory of Grenoble using various interconnection schemes are presented.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

Bond graph model of a PEM fuel cell stack

2008 ◽  
Vol 1 (06) ◽  
pp. 329-334
Author(s):  
S. Rabih ◽  
C. Turpin ◽  
S. Astier
Keyword(s):  
Fuel Cell ◽  
Graph Model ◽  
Pem Fuel Cell ◽  
Bond Graph ◽  
Fuel Cell Stack
Sign in / Sign up

Export Citation Format

Share Document