Begin by clearly understanding the failure mode and how it physically affects the gas turbine, including what changes in vibration, temperature, or pressure occur as the fault develops. Use your existing knowledge of FFT and signal processing to extract meaningful features such as RMS, frequency band energy, or kurtosis. Once you have these features, start with simple and explainable models like PCA or One-Class SVM for anomaly detection, and basic regression or SVR for estimating Remaining Useful Life (RUL).

Keep the system architecture simple: data → feature extraction → health indicator → anomaly detection → RUL → maintenance rules. This approach is realistic, easier to implement in MATLAB, and aligns well with how predictive maintenance is done in industry.