Intelligent fault diagnosis method for marine diesel engines using instantaneous angular speed
Intelligent fault diagnosis method for marine diesel engines using instantaneous angular speed
复制标题
DOI：
10.1007/s12206-012-0621-2
复制
发表时间：
2012-08
影响因子：
1.6
通讯作者：
Zhixiong Li;Xin-ping Yan;C. Yuan;Zhongxiao Peng
中科院分区：
文献类型：
--
作者：
Zhixiong Li;Xin-ping Yan;C. Yuan;Zhongxiao Peng
船用柴油机的正常运行保证了一次航行的预定完成和效率。任何故障都可能导致重大的经济损失和严重的事故。因此，可靠、及时地监测发动机状态，以防止机组发生故障是至关重要的。本文描述并评估了一种基于经验模态分解（EMD）、核独立分量分析（KICA）、Wigner双谱和支持向量机（SVM）集成的智能诊断技术的发展和应用。这是对利用瞬时角速度（IAS）进行柴油机故障检测工作的扩展。为了解决欠定盲源分离（BSS）问题，本文首先提出了结合EMD和KICA对单通道IAS传感器的IAS信号进行估计的方法。KICA也被用于选择由Wigner双谱提取的特征。然后将支持向量机应用于船用柴油机故障的多类智能识别。利用6缸发动机模型和航军20舰实测IAS数据对该方法进行了数值模拟。数值和实验结果表明，该方法具有较高的诊断效率。利用EMD-KICA和Wigner双谱提取了IAS信号明显的故障特征，支持向量机的故障检出率超过94.0%。因此，该方法对船用柴油机的故障诊断具有可行性和实用性。
The normal operation of marine diesel engines ensures the scheduled completion and efficiency of a trip. Any failures may result in significant economic losses and severe accidents. It is therefore crucial to monitor the engine conditions in a reliable and timely manner in order to prevent the malfunctions of the plants. This work describes and evaluates the development and application of an intelligent diagnostic technique based on the integration of the empirical mode decomposition (EMD), kernel independent component analysis (KICA), Wigner bispectrum and support vector machine (SVM). It is an extension of the previous work on the fault detection for a diesel engine using the instantaneous angular speed (IAS). In this study, in order to solve the underdetermined blind source separation (BSS) problem the combination of EMD and KICA is firstly presented to estimate IAS signals from a single-channel IAS sensor. The KICA is also applied to select distinguished features extracted by Wigner bispectrum. The SVM is then employed for the multi-class recognition of the marine diesel engine faults in an intelligent way. Numerical simulations using a 6-cylinder engine model and real IAS data measured on the ship named “Hangjun 20” are used to evaluate the proposed method. Both the numerical and experimental diagnostic results have shown high efficiency of the proposed diagnostic method. Distinct fault features of the IAS signals have been extracted by the EMD-KICA and Wigner bispectrum, and the fault detection rate of the SVM is beyond 94.0%. Thus, the proposed method is feasible and available for the fault diagnosis of marine diesel engines.