A Flight Expert System (FLES) For On-Board Fault Monitoring And Diagnosis

1986 ◽  
Author(s):  
M. Ali ◽  
D. .. Scharnhorst ◽  
C. S. Ai ◽  
H. J. Ferber
Keyword(s):  
Expert System ◽  
Monitoring And Diagnosis ◽  
Fault Monitoring

General Fault Monitoring and Diagnosis Expert System Based on Fault Tree and Multi-Sensor Information

2011 ◽  
Vol 121-126 ◽  
pp. 4481-4485
Author(s):  
Ai Yu Zhang ◽  
Xiao Guang Zhao ◽  
Lei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Expert Knowledge ◽  
Sensor Failure ◽  
Fault Trees ◽  
Intelligent Algorithms ◽  
Monitoring And Diagnosis ◽  
Backward Reasoning ◽  
Fault Monitoring ◽  
Sensor Information

Due to the limited generality of traditional fault diagnosis expert system and its low accuracy of extracting failure symptoms, a general fault monitoring and diagnosis expert system has been built. For different devices, users can build fault trees in an interactive way and then the fault trees will be saved as expert knowledge. A variety of sensors are fixed to monitor the real-time condition of the device and intelligent algorithms such as wavelet transform and neural network are used to assist the extraction of failure symptoms. On the basis of integration of multi-sensor failure symptoms, the fault diagnosis is realized through forward and backward reasoning. The simulation diagnosis experiments of NC device have shown the effectiveness of the proposed method.

Intelligent Systems in aerospace

1990 ◽  
Vol 5 (3) ◽  
pp. 147-166 ◽  
Author(s):  
Moonis Ali
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Planning And Scheduling ◽  
Monitoring And Diagnosis ◽  
Aerospace Applications ◽  
Fault Monitoring ◽  
Aerospace Systems ◽  
Operations And Maintenance ◽  
And Control ◽  
And Robotics

AbstractAn overview of research in the areas of aerospace applications of artificial intelligence, expert Systems, neural networks and robotics is presented. Challenges associated with aerospace projects require increasingly complex aerospace Systems which in turn demand automation and fault tolerance. We have addressed these issues and provided a survey of the research on intelligent Systems that has been carried out in an attempt to meet these challenges. The application areas we have overviewed include fault monitoring and diagnosis, generation and management of power in space, efficient and effective command and control, operations and maintenance of space stations, planning and scheduling, automation, and cockpit management.

Expert System Based on Fuzzy Rules for Monitoring and Diagnosis of Operation Conditions in Rotating Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 950-960
Author(s):  
Rui Francisco Martins Marçal ◽  
Kazuo Hatakeyama ◽  
Dani Juliano Czelusniak
Keyword(s):  
Expert System ◽  
Detection Method ◽  
Fuzzy Rules ◽  
Spectral Signature ◽  
Linguistic Variables ◽  
Rotating Machines ◽  
Rotating System ◽  
Monitoring And Diagnosis ◽  
Operation Conditions ◽  
Vibration Pattern

This work provides a detection method for failure in rotating machines based on a change of vibration pattern and offers the diagnosis about the operation conditions using Fuzzy Logic. A mechanic structure (as an experimental prototype where faults can be inserted) called Rotating System has been used. The vibration standard of the Rotating System, called "The Spectral Signature", has been obtained. The changes in the vibration standard have been analyzed and used as parameters for detecting incipient failures, as well as their condition evolution, allowing predictive monitoring and planning of maintenance. The faults analyzed in this work are caused due to insertion of asymmetric masses for unbalancing in the axle wheel. The system for diagnosing Fuzzy System was calibrated to detect and diagnose the conditions: normal, incipient failure, maintenance, and danger, using linguistic variables. The frequency of rotation and the amplitudes of vibration of the axle wheel are considered in each situation as parameters for analysis, diagnostic, for the decision by the Expert System based on Fuzzy rules. The results confirm that the proposed method is useful for detecting incipient failures, monitoring the evolution of severity and offering grants for planning and decision making about maintenance or prevention of rotating machines.

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