Health Monitoring of an Auxiliary Power Unit Using a Classification Tree
The objective of this work is to present a method to monitor the health of Auxiliary Power Units (APU) using a Dynamic Computational Model, Gas Path Analysis and Classification and Regression Trees (CART). The main data used to train the CART consists of measurements of the exhaust gas temperature, the bleed pressure and the fuel flow. The proposed method was tested using actual APU data collected from a prototype aircraft. The method succeeded in classifying several relevant fault conditions. The few misclassification errors were found to be due to the insufficiency of the information content of the measurement data. *
Physics-based Model and Neural Network Model for Monitoring Starter Degradation of APU
An electric starter provides the initial power to run the Auxiliary Power Unit (APU) during the startup process. With the starter degradation, its output power declines, which affects the APU starting performance, and eventually leads to the starting failure. Previous works have attempted to estimate the starter degrading trend, however a clear symptom for the starter degradation is not provided, which can be instrumental for the preventive maintenance. This paper develops a physics-based transient model to assess the starter degradation using the gas-path measurements of the APU. To overcome shortcomings for the lack of component characteristics, a generic modeling approach is adopted. For the comparative study, a back-propagation, feedforward neural network model is structured, trained and tested. Both models are implemented in the nominal and degraded conditions, and their capabilities as monitoring tools for the starter degradation are verified. The physics-based approach provides more accurate results for the cases with degraded starters, whereas the neural network model shows superior results with the starters in healthy condition.
Die Populationsdynamik Von Canthocampt Us Staphylinus (Jurine) (Copepoda, Harpacticoida) Im Litoral Des Bodensees
The population dynamics of Canthocamptus staphylinus in the littoral zone of Lake Constance are described (fig. 2). Adult animals aestivate as cysts. The main part of the population emerges from the cysts in autumn. During the resting stage and the first spell after emerging, mortality is very low (fig. 3). Reproduction, expressed as numbers of ovigerous females, begins in November and reaches its maximum in January. The new generation becomes adult in May/June and encysts without previous reproduction. According to Sarvala (1979) it may be possible, that in littoral populations dormancy is induced by long day periods and temperatures of over 12° C (figs. 1, 2). In the profundal zone C. staphylinus also forms cysts, however, both light and temperature are not likely to induce this stage in depths of over 60 m.