Spanish 
Chinese 
Russian 
German 
French 
Japanese 
Portuguese 
Hindi Development of a Modified Extended Model Based Fault Detection and Diagnosis Approach
*Corresponding Author: Alireza Alikhani, Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran, Tel: 989124880507, Email: aalikhani@ari.ac.irReceived Date: Oct 27, 2022 / Published Date: Feb 17, 2023
Citation: Alikhani A, Sharifi G (2023) Development of a Modified Extended Model Based Fault Detection and Diagnosis Approach. Diagnos Pathol Open 8:215
Copyright: © 2023 Alikhani A, et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
| Peer-reviewed Full Article 
Abstract
A supervisory system for space missions is critical due to the high risk of missions, the costs and the impossibility of adding redundancy. The model based fault detection approaches are of interest due to their highly responsive speed, robustness against disturbances and uncertainties and accuracy. Conventional model based methods have some drawbacks such as feasibility and applicability. In this paper, a Modified Extended Multiple Models Adaptive Estimation (MEMMAE) method is developed which keep both the advantages of the previous model based methods and take into account some limitations. This approach can be performed on various systems to detect and diagnose faults, with appropriate response speed and resistance to uncertainty and disturbances. By combining the recursive least square algorithm with the Extended Multiple Model Adaptive Estimation (EMMAE) method, the limitations of this method including simultaneous fault detection, diagnostics of failure cause and high processing volume are eliminated. The method is implemented on a spacecraft as a case study using the MATLAB/SIMULINK software and demonstrates that the responsive speed and accuracy of the proposed method is significantly much more effective and accurate than the previous method.
