A novel approach for marine diesel engine fault diagnosis

作者：Cai, Chengtao^*; Weng, Xiangyu; Zhang, Chuanbin

来源：Cluster Computing, 2017, 20(2): 1691-1702.

DOI：10.1007/s10586-017-0748-0

• Marine diesel engine
• Fault diagnosis
• Support vector machine
• Association rule mining

This paper proposes a novel approach for marine diesel engine fault diagnosis based on data mining. First, a marine diesel engine is divided into four subsystems according to the basic structure and fault features of the engine, which overcomes the difficulty of fault diagnosis caused by the structure complexity. Second, fault diagnosis of each subsystem is performed accordingly by employing the support vector machine algorithm, and the classification models are trained using the historical fault features. Third, association analysis of the fault features is achieved efficiently by applying the association rule mining algorithm and the implied relationship of faults among subsystems are mined using the transaction database of faults. A simulation system is implemented in Matlab to achieve real-time marine diesel engine state monitoring and fault diagnosis.

• 出版日期2017-6
• 单位哈尔滨工程大学