为给船舶主机整体性能的状态监测和健康管理提供更好的技术支持，提出一种基于深度置信网络（DBN）和支持向量回归机（SVR）相结合的船舶主机排烟温度基线模型，通过计算设备运行时的状态参数测量值与基线值的偏差量，并结合偏差量的变化情况，进而判断出部件的退化或故障情况。以某轮2015年1—6月份的航行数据和试航报告中的数据作为初始样本，经异常值剔除、噪声去除和稳定点筛选等处理后，形成最终样本集；利用DBN网络对样本数据进行特征提取，随后将提取的数据特征输入SVR中，建立船舶主机排烟温度基线模型。结果表明：通过测试集验证，基于参数调优的DBN-SVR模型相比BP模型、DBN模型和SVR模型能更准确地预测状态参数，拥有优良的稳定性。

In order to provide better technical support for the condition monitoring and health management of the overall performance of main engine, the exhaust gas temperature baseline model based on the combination of deep belief network (DBN) and support vector regression (SVR) was proposed. By calculating the deviation between the measured value and the baseline value, and combining the change of the deviation, the degradation or fault of the components could be judged. Taking the navigation data of a ship from January to June 2015 and the data in the trial report as the initial sample, the final dataset was formed after processing as abnormal value elimination, noise removal and stability point extraction, etc. The DBN was used to extract the features of the dataset, and then the extracted data features were input into the SVR to establish the baseline model of main engine exhaust gas temperature. The results show that by verification of the test set, the DBNSVR model based on parameter optimization can predict the state parameters more accurately than BP model, DBN model and SVR model, and has excellent stability.