Condition monitoring of engine gas generators plays an essential role in airline fleet management. Adaptive diagnostic systems are becoming available that interpret measured data, furnish diagnosis of problems, provide a prognosis of engine health for planning purposes, and rank engines for scheduled maintenance. More than four hundred operations worldwide currently use versions of the first or second generation diagnostic tools. Development of a third generation system is underway which will provide additional system enhancements and combine the functions of the existing tools. Proposed enhancements include the use of artificial intelligence to automate, improve the quality of the analysis, provide timely alerts, and the use of an Internet link for collaboration. One objective of these enhancements is to have the intelligent system do more of the analysis and decision making, while continuing to support the depth of analysis currently available at experienced operations. This paper presents recent developments in technology and strategies in engine condition monitoring including: 1) application of statistical analysis and artificial neural network filters to improve data quality; 2) neural networks for trend change detection, and classification to diagnose performance change; and 3) expert systems to diagnose, provide alerts and to rank maintenance action recommendations.

Condition monitoring of engine gas generators plays an essential role in airline fleet management. Adaptive diagnostic systems are becoming available that interpret measured data, furnish diagnosis of problems, provide a prognosis of engine health for planning purposes, and rank engines for scheduled maintenance. More than four hundred operations worldwide currently use versions of the first or second generation diagnostic tools. Development of a third generation system is underway which will provide additional system enhancements and combine the functions of the existing tools. Proposed enhancements include the use of artificial intelligence to automate, improve the quality of the analysis, provide timely alerts, and the use of an Internet link for collaboration. One objective of these enhancements is to have the intelligent system do more of the analysis and decision making, while continuing to support the depth of analysis currently available at experienced operations. This paper presents recent developments in technology and strategies in engine condition monitoring including: (1) application of statistical analysis and artificial neural network filters to improve data quality, (2) neural networks for trend change detection, and classification to diagnose performance change, and (3) expert systems to diagnose, provide alerts and to rank maintenance action recommendations.

Directional traps, both horizontal and vertical, were used to assess the behavioural impact of phenanthrene application on soil springtail communities. Avoidance was not detected. Rather, a vertical attraction of the dominant species, Folsomia manolachei, was demonstrated, as well as a decrease in horizontal movements of Lepidocyrtus lanuginosus, another important species mainly captured at the soil surface. Ecological consequences of the results are discussed.

A revolutionary approach to gas turbine condition monitoring is made possible by the recent development of accurate real-time gas turbine performance models. This paper describes an approach for an integrated condition management system operating concurrently with the gas turbine control system for improved availability, safety and economy. This paper considers the system subject to the requirements and constraints of aircraft gas turbines. A system architecture is described based on a primary, gas path performance model with supplementary models representing the secondary air, fuel and lubrication systems and the rotor system dynamics. Measurement and processing requirements for the system are defined. Preflight, in-flight and postflight application and analysis by the gas turbine operator are discussed.

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