This article presents a structured analytical methodology for diagnosing performance variability and reliability-related behaviour in GE’s LM6000 gas turbines using time-series operational data. The approach integrates statistical screening, information-theoretic analysis, and machine learning techniques to identify key variables influencing turbine performance under varying operating conditions.
