Research and Applications of Condition Monitoring and Predictive Maintenance of Marine Diesel Engines

doi:10.3969/j.issn.1004-132X.2022.10.004

Abstract

Abstract: Based on the four dimensions of thermal-pressure parameters， lubricant oil conditions， vibrations and cylinder pressures， data acquisition and feature extraction were carried out， and a method for diesel engine condition monitoring was proposed based on OCSVM anomaly detection algorithm and Fisher discriminant analysis. The CUSUMMR was used for parameter trend detection and the D-S evidence theory and weight of evidence method were used for multi-source information fusion. the RUL(remaining useful life) of diesel engine lubricating oil was predicted by LSTM. The Paper solves the problems such as low early warning rate， poor adaptability of the model under diesel engine dynamic conditions.

Key words: feature extraction, condition monitoring, predictive maintenance, change point detection

摘要： 从热工、油液、振动、缸压4个维度开展数据采集和特征提取工作，提出了OCSVM异常检测算法联合Fisher判别分析进行柴油机状态监测的一种方法，采用累计和/参数均值比进行参数变点检测，采用D-S证据理论及证据加权分配合成方法进行多源信息融合，并利用LSTM对柴油机滑油的剩余使用寿命进行预测，解决了传统柴油机状态监测方法早期预警率低、动态工况下模型适应性差等问题。

关键词: 特征提取, 状态监测, 预测性维护, 变点检测

CLC Number:

U676.4

CHEN Dongmei, ZHAO Siheng, WEI Chengyin, CHEN Yajie. Research and Applications of Condition Monitoring and Predictive Maintenance of Marine Diesel Engines[J]. China Mechanical Engineering, 2022, 33(10): 1162-1168.

陈冬梅, 赵思恒, 魏承印, 陈亚杰. 船舶柴油机状态监测及预测性维护研究及应用[J]. 中国机械工程, 2022, 33(10): 1162-1168.

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