基于瞬时转速和机器学习的船用柴油机健康状态评估

Evaluation of the Health of Marine Diesel Engines Based on Instantaneous Angular Speed and Machine Learning

在一台Z6170型船用中速柴油机上进行了发动机故障模拟试验。首先,将不同工况下监测的瞬时转速信号的多维特征参数进行极差(MIN-MAX)标准化处理,并利用t分布领域嵌入(t-SNE)算法对特征参数进行降维处理。然后,采用随机森林(RF)算法建立了发动机气缸故障诊断模型。结果显示,监测蕴含柴油机运行状态丰富信息的瞬时转速信号,采用t-SNE和RF算法建立的柴油机运行状况评估及气缸故障诊断模型可以有效评估发动机健康状态,以保证其安全可靠运行。

An engine failure simulation test was carried out on a Z6170 marine medium speed diesel engine.The multi-dimensional characteristic parameters of instantaneous speed signal monitored under different operating conditions were normalized by the MIN-MAX method,and the t-distributed stochastic neighbor embedding(t-SNE)algorithm was used to reduce the dimensionality of the characteristic parameters.The random forest(RF)algorithm was used to establish an engine cylinder fault diagnosis model.Results show that based on the monitoring of instantaneous speed,which contains rich information of engine operating condition,the evaluation of diesel engine operating conditions and the cylinder fault diagnosis model established by the t-SNE and RF algorithm can effectively evaluate the health condition of an diesel engine,so as to quarantee it running safely and reliably.