Fault Diagnosis for Marine Diesel Engine Based on Class Label Probability and Random Walk

Abstract

Cluster methods can be used for fault diagnosis of marine diesel engine. We propose a cluster method by assigning a class label to each sample and updating it according to the class label probability. Due to the random walk process in cluster process, multiple results are obtained, and some classes can be found more easily. Fault diagnosis simulation experiment is executed on a self-manufactured marine engine room simulator to verify the method performance, and we obtain the correct graphical and number results. Both fault patterns in fault history and new fault pattern can be recognized.