Anomaly detection and confidence interval-based replacement in decay state coefficient of ship power system

被引:1
|
作者
Chang, Xingshan [1 ,2 ,3 ]
Yan, Xinping [1 ,2 ,3 ,4 ]
Qiu, Bohua [5 ,6 ,7 ]
Wei, Muheng [5 ]
Liu, Jie [8 ]
Zhu, Hanhua [8 ]
机构
[1] Wuhan Univ Technol, State Key Lab Maritime Technol & Safety, Wuhan 430063, Hubei, Peoples R China
[2] Wuhan Univ Technol, Natl Water Transportat Safety Engn Technol Res Ctr, Wuhan, Hubei, Peoples R China
[3] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Hubei, Peoples R China
[4] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan, Hubei, Peoples R China
[5] ZhenDui Ind Artificial Intelligence Co Ltd, Shenzhen, Guangdong, Peoples R China
[6] Shanghai Jiao Tong Univ, Dept Automat, Shanghai, Peoples R China
[7] Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai, Peoples R China
[8] Wuhan Univ Technol, Sch Naval Architecture Ocean & Energy Power Engn, Wuhan, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
anomaly trend detection and prediction; confidence interval estimation; decay state coefficient; marine vehicles; ship power system;
D O I
10.1049/itr2.12581
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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. This article introduces the YC3Model, a novel approach for anomaly detection and predictive replacement in the degradation decay state coefficient of ship power system. Leveraging a dynamic triple sliding window mechanism and Gaussian process regression, the YC3Model enhances operational safety and maintenance efficiency by improving the accuracy of anomaly detection and providing reliable confidence intervals for predictive replacements. The findings demonstrate the model's superior performance over traditional methods, making it a valuable tool for the intelligent operation and maintenance of ship power system. image
引用
收藏
页码:2409 / 2439
页数:31
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