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The anomaly detection and predictive replacement of the degradation decay state coefficient (Desc) of ship power system (SPS) are crucial for ensuring their operational safety and maintenance efficiency. This study introduces the YC3Model, a model based on a dynamic triple sliding window mechanism, and Gaussian process regression) to address this challenge. It combines the temporal variation characteristics of the decay state coefficient's original data, first‐order, and second‐order differential data in both normal and abnormal trend intervals. The model calculates three local statistical measures within each sliding window and employs the Z‐score method for anomaly detection. The combination of three sliding windows reduces false positives and negatives, enhancing the precision of anomaly detection. For detected anomalies, Gaussian process regression is used for prediction and replacement, providing confidence intervals to increase the reliability of the predicted values. Experimental results demonstrate that the YC3Model exhibits superior anomaly detection accuracy and adaptability in the degradation process of SPS, surpassing traditional methods across a range of evaluation metrics. This confirms the potential of YC3Model in health monitoring and predictive maintenance of SPS, offering reliable data input for the intelligent operation and maintenance (IO&M) of SPS. [ABSTRACT FROM AUTHOR]

Abstract The anomaly detection and predictive replacement of the degradation decay state coefficient (Desc) of ship power system (SPS) are crucial for ensuring their operational safety and maintenance efficiency. This study introduces the YC3Model, a model based on a dynamic triple sliding window mechanism, and Gaussian process regression) to address this challenge. It combines the temporal variation characteristics of the decay state coefficient's original data, first‐order, and second‐order differential data in both normal and abnormal trend intervals. The model calculates three local statistical measures within each sliding window and employs the Z‐score method for anomaly detection. The combination of three sliding windows reduces false positives and negatives, enhancing the precision of anomaly detection. For detected anomalies, Gaussian process regression is used for prediction and replacement, providing confidence intervals to increase the reliability of the predicted values. Experimental results demonstrate that the YC3Model exhibits superior anomaly detection accuracy and adaptability in the degradation process of SPS, surpassing traditional methods across a range of evaluation metrics. This confirms the potential of YC3Model in health monitoring and predictive maintenance of SPS, offering reliable data input for the intelligent operation and maintenance (IO&M) of SPS.