Realtime diagnostic prognostic solution for life cycle management of thermomechanical system

Data based approach and methodology for real time diagnosis and prognosis solutions for thermomechanical systems is discussed. Coated turbine blade operation is emulated to sensor online temperature data as the real-time inputs for the software code developed. An algorithm is presented first and extended sampling based statistical hypothesis tests are used for anomaly detection tests. Paired t-test and rank sum hypotheses test are found to be appropriate for different data set combinations. Matlab statistical package is used for the software code. The algorithm and methodology work well with laboratory temperature data and seeded faults. Few limitations of the developed system code are identified.