Automatic programming methodologies for electronic hardware fault monitoring

This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the Stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.This research was supported by the International Joint Research Grant of the IITA (Institute of Information Technology Assessment) foreign professor invitation program of the MIC (Ministry of Information and Communication), Korea

Fault monitoring

Information on fault monitoring of aircraft systems is given in viewgraph form. Information is given on monitor architecture and benefits, implementation characteristics, and an engine monitoring and control system

A route generator concept for aircraft onboard fault monitoring

Because of the increasingly complex environments in which the flight crews of commercial aviation aircraft must operate, a research effort is currently underway at NASA Langley Research Center to investigate the potential benefits of intelligent cockpit aids, and to establish guidelines for the application of artificial intelligence techniques to advanced flight management concepts. The segment of this research area that concentrates on automated fault monitoring and diagnosis requires that a reference frame exist, against which the current state of the aircraft may be compared to determine the existence of a fault. This paper describes a computer program which generates the position of that reference frame that specifies the horizontal flight route

A survey of broken rotor bar detection using PT and HT in squirrel cage electrical machine

Early detection of faults in electrical machines are imperative because of their diversity of use in different fields. A suitable fault monitoring scheme helps to stop propagation of the failure or limit its escalation to severe degrees and thus prevents unscheduled downtimes that cause loss of production and financial income. In this study, a survey of methods based on the Park transform and Hilbert transform for broken rotor bar fault monitoring in Squirrel cage electrical machine is presented

BEARING FAULT MONITORING SYSTEM USING ACOUSTIC EMISSION

Detecting mechanical faults in bearings and machinery has long been recognized as being important for preventing catastrophic failure and effective maintenance planning. The human senses of sound and touch were the first mechanisms used to detect machinery problems. Electronic sensors have since offered the ability to feel and listen to machinery with more precision, at more locations, and over more time than was ever before possible. Interpretation of the electronic signals delivered by sensors has provided the maintenance engineer with the diagnostic information necessary to pinpoint bearing faults, thus enabling a more efficient and predictable maintenance effort. However, skilled and trained personnel have been required to effectively interpret this diagnostic information. As electronic sensors have become more sophisticated, and also have the diagnostic techniques, leading to the ability of earlier detection of failures with less required skill. This project proposes an implementation of bearing fault monitoring system by using acoustic emission (AE). The signal is captured and analyzed by MATLAB software. Generally this project is carried Oft by detecting ultrasonic waves captured by AE sensor of tested bearing and interprets the data which mean either the bearing is healthy or havin~ defect. The benefit of having early detection of bearing fault will be able to save maintenance cost and fso human life