为提升多发故障信号监测效果，提出多传感器融合的船舶轮机设备多发故障信号监测方法。多个传感器采集船舶轮机设备运行信号，通过经验小波变换提取轮机设备多发故障信号特征；神经网络根据故障信号特征得到单个传感器的多发故障信号监测结果，将单个传感器的监测结果为基本概率分配函数，根据证据理论获取最终的船舶轮机设备多发故障信号监测结果。实验证明：该方法可有效采集船舶轮机设备运行信号，并提取多发故障信号特征；该方法可有效检测多发故障信号，具备较高的多发故障信号监测精度。
To improve the monitoring effect of multiple fault signals, a multi-sensor fusion monitoring method for multiple fault signals of marine engine equipment is proposed. Multiple sensors collect the operation signals of marine engine equipment, and extract the characteristics of multiple fault signals of marine engine equipment through empirical wavelet transform. The neural network obtains the monitoring results of multiple fault signals of a single sensor according to the characteristics of fault signals. The monitoring results of a single sensor are regarded as the basic probability distribution function, and the final monitoring results of multiple fault signals of marine engine equipment are obtained according to the evidence theory. The experimental results show that this method can effectively collect the operation signals of marine engine equipment and extract the characteristics of multiple fault signals. This method can effectively detect multiple fault signals and has high monitoring accuracy.
2022,44(17): 114-117 收稿日期：2022-05-17
DOI：10.3404/j.issn.1672-7649.2022.17.022
分类号：TP206+.3
作者简介：赵云博(1984-)，男，讲师，主要从事轮机工程技术研究
参考文献：
[1] 田慧, 林叶锦, 张均东, 等. 基于单分类算法OSVM船用燃气轮机状态评估[J]. 船舶工程, 2020, 42(7): 152–156
TIAN Hui, LIN Yejin, ZHANG Jundong, et al. Condition evaluation of marine gas turbine based on single classification algorithm OSVM[J]. Ship Engineering, 2020, 42(7): 152–156
[2] 赵骏, 朱嵘嘉, 陈鹏, 等. 基于模糊状态观测器的燃气轮机转速传感器故障检测研究[J]. 传感技术学报, 2019, 32(8): 1227–1231
ZHAO Jun, ZHU Rong-jia, CHEN Peng, et al. Study on speed sensor fault diagnosis for gas turbine based on fuzzy state observer[J]. Chinese Journal of Sensors and Actuators, 2019, 32(8): 1227–1231
[3] 曾友渝, 谢强. 基于改进RNN和VAR的船舶设备故障预测方法[J]. 计算机科学, 2021, 48(6): 184–189
ZENG You-yu, XIE Qiang. Fault prediction method based on improved RNN and VAR for ship equipment[J]. Computer Science, 2021, 48(6): 184–189
[4] 李汶骏, 龙伟, 曾力, 等. 基于差分进化和核主元分析的燃气轮机故障检测[J]. 四川大学学报(自然科学版), 2021, 58(2): 83–89
LI WenJun, LONG Wei, ZENG Li, et al. Fault detection of gas turbine air path system based on KPCA and DE[J]. Journal of Sichuan University (Natural Science Edition), 2021, 58(2): 83–89
[5] 杨新, 于佐东, 张志远, 等. 基于多特征提取与核极限学习机的汽轮机转子故障诊断[J]. 汽轮机技术, 2020, 62(2): 137–142
YANG Xin, YU Zuodong, ZHANG Zhiyuan, et al. Fault diagnosis of steam turbine rotor based on multi feature extractions and kernel extreme learning machine[J]. Turbine Technology, 2020, 62(2): 137–142
