Experimental validation of statistical algorithm for diagnosis of damage fault

A statistical algorithm was developed for the damage fault diagnosis and prognosis tool and the present work focuses on the experimental validation. The oxide scale growth experiments using laboratory samples under thermal cycling simulate the hot section turbine blade coating failures. The experimental steps, oxide thickness data measurement, collection and sampling procedures are discussed. Three data samples each from two groups under different thermal cycling conditions are considered. The data are subjected to randomness check, preprocessing, rank sum test etc. The validation is carried out with 15 possible combinations for analysis. Consistent with the mean thickness distribution for the samples in two groups, the statistical algorithm for damage and anomaly diagnosis yields expected results.

• Type: Conference
• Year: 2009
• Where: CCECE
• Authors: Amar Kumar, Amiya Nayak, Alka Srivastava, Nita Goel
• Keywords: CCECE 2009, Damage Fault Diagnosis, Electrical And Computer Engineering, Statistical Algorithm, Thermal Cycling