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. *
Aircraft bleed valve fault classification using support vector machines and classification trees
This work consisted in developing a data driven solution to identify and isolate faults on aircraft bleed valves using supervised machine learning algorithms. A physical computer model of the valve with noisy observations and variable environmental conditions was used to simulate a set of operationing conditions submitted to three different failures modes: leakage between the valve chambers, degraded return spring and excessive piston friction. Features extracted from the model simulations were used to train and test a Support Vector Machine (SVM) as well as a Classification Tree. Different SVM Kernels and tree pruning limits were considered at the training step. Both solutions were evaluated at the testing step by evaluating their accuracy and precision. The results evidenced that both algorithms could identify and classify the faults with a slightly better performance of the SVM. Also, results evidenced that this application could automate the fault isolation process of these types of valves. This work evidences the importance of the proper definition of the classification algorithms as well as their metaparameters in order to obtain the most suitable solution for fault isolation developments.
An Analysis Procedure for the Validation of On-Site Performance Measurements of Gas Turbines
In order to ensure accuracy and credibility of an engine health monitoring system, it is vital that the measurements used in the analysis process be accurate. An analysis procedure has been developed to verify the accuracy of daily measurements taken from industrial gas turbines in the gas pipeline industry. The procedure has been extended to use the measurements to develop an engine health monitoring system for this class of equipment which is based solely on existing instrumentation. The method has been tested using field data from several gas pumping units and was found to be capable of isolating instrumentation problems and to correctly diagnose engine health deterioration.