Real-time, integrated health monitoring of turbomachinery that can detect, classify, and predict developing faults is critical to reducing operating and maintenance costs while optimizing the life of critical components. Statistical-based anomaly detection algorithms, fault pattern recognition techniques and advanced probabilistic models for diagnosing structural, performance and vibration redlated faults and degradation can now be developed for real-time monitoring environments. Integration and implementation of these advanced technolgoies presents a great opportunity to significantly enhance current health monitoring capabilities and risk management practices.
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