关键词: 极限学习机, 多属性特征, 故障诊断

Abstract: The traditional deep kernel extreme learning machine network only uses the end-layer feature and pays little attention to optimizing the choice of kernels in fault diagnosis of marine diesel engine. To solve the problem, a fault diagnosis of marine diesel engine based on multi-layer feature expression and multiple kernel extreme learning machine is proposed. The multi-layer features of faults are extracted using deep extreme learning machine network. A multi-attribute fault feature vector is combined by the extracted feature vectors of each layer. The multiple kernel extreme learning machine classifier is used to realize the diagnosis of faults. Comparing the proposed method with other extreme learning machine methods on some benchmark classification datasets and artificial faults sets of marine diesel engine, experimental results show that the proposed method is effective to improve the accuracy and stability and it has strong generalization ability in fault diagnosis, it proves to be a good tool for fault diagnosis of marine diesel engine.

Key words: extreme learning machine, multi-attribute feature, fault diagnosis