Real-time, integrated health monitoring of gas turbine engines that can detect, classify, and predict developing engine faults is critical to reducing operating and maintenance costs while optimizing the life of critical engine components. Statistical-based anomaly detection algorithms, fault pattern recognition techniques and advanced probabilistic models for diagnosing structural, performance and vibration related faults and degradation can now be developed for real-time monitoring environments. Integration and implementation of these advanced technologies presents a great opportunity to significantly enhance current engine health monitoring capabilities and risk management practices.
This paper describes some novel diagnostic and prognostic technologies for dedicated, real-time sensor analysis, performance anomaly detection and diagnosis, vibration fault detection, and component prognostics. The technologies have been developed for gas turbine engine health monitoring and prediction applications which includes an array of intelligent algorithms for assessing the total ‘health’ of an engine, both mechanically and thermodynamically. This includes the ability to account for uncertainties from engine transient conditions, random measurement fluctuations and modeling errors associated with model-based diagnostic and prognostic procedures. The implementation of probabilistic methods in the diagnostic and prognostic methodology is critical to accommodating for these types of uncertainties.
Development of big data lean optimisation using different control mode for Gas Turbine engine health monitoring
