Multiple faults diagnosis for ocean-going marine diesel engines based on different neural network algorithms
Guoqing Zhu, Lin Huang, Jiapeng Yin, Wen Gai, Lijiang Wei
Abstract
Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely involved. Due to the strong correlation, non-linearity and randomness of multiple faults, it is extremely difficult to make an accurate diagnosis. In this study, diagnosis methods based on thermal parametric analysis combined with different neural network algorithms were established and used for the diagnosis of multiple faults in the ocean-going marine diesel engine. The results show that the Levenberg Marquardt back propagation neural network has the highest diagnostic accuracy rate of 88.89% and 100% for multiple faults and single faults, respectively, and its diagnostic time is also relatively short, 0.78 s. The Bayesian regularization back propagation neural network can give a diagnostic accuracy rate of 100% for single faults, but for multiple faults, the diagnostic accuracy rate is only 55.56%, and the diagnosis time for the entire sample is the longest. As for the probabilistic neural network, although it has the fastest diagnosis speed, it has the lowest diagnostic accuracy rate for both single faults and multiple faults. The results may provide references for the online diagnosis of single faults and multiple faults in ocean-going marine diesel engines.