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Real-Time Fault Identification for Developmental Turbine Engine Testing

1997; Linguagem: Inglês

10.1115/97-gt-141

Donald J. Malloy, Mark A. Chappell, C. Biegl,

Scientific Measurement and Uncertainty Evaluation

Hundreds of individual sensors produce an enormous amount of data during developmental turbine engine testing. The challenge is to ensure the validity of the data and to identify data and engine anomalies in a timely manner. An automated data validation, engine condition monitoring, and fault identification process that emulates typical engineering techniques has been developed for developmental engine testing. An automated data validation and fault identification approach employing engine cycle-matching principles is described. Engine cycle-matching is automated by using an adaptive nonlinear component-level computer model capable of simulating both steady-state and transient engine operation. An automated model calibration process is also described. The model enables automation of traditional data validation, engine condition monitoring, and fault identification procedures. A distributed parallel computing approach enables the entire process to operate in realtime. The result is a capability to detect data and engine anomalies in realtime during developmental engine testing. The approach is shown to be successful in detecting and identifying sensor anomalies as they occur and distinguishing these anomalies from variations in component and overall engine aerothermodynamic performance.