Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Research on fault features of main propulsion system based on correlation weight matrix
Yiting Zhan1, Tao He2, Weixi Huang2, Ji Zeng1(1.College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai，China
2. China Ship Scientific Research Center, Wuxi, China.)

to establish a warning model, which could warn of continuous increases
in exhaust temperature (Tian, 2016), and an expert diagnostic model basedon a fuzzy neural network was applied in marine power plant systemfault diagnosis(Tang, 2018). BP neural networks were also used tosimulate different fault conditions to improve ship safety (Ozturk, 2016).
However, cruise ship fault prediction differs from that of ordinaryships. Due to its multiple specialties, system and monitoring variables
along with changing navigational conditions, propulsion equipment fault
prediction has become more complex. On one hand, if only one particularequipment fault model was considered, any correlation or chain reaction
between faults would be overlooked, which would disconnect theinteraction between various systems and multi-level complex intelligent
diagnosis would be impossible. On the other hand, the advantage of big
data analysis is that when there is sufficient data, the relationshipbetween data can be revealed without making assumptions (Qian, 2020).
Therefore, this paper studied cruise operational and maintenance big data,
supported by the correlation between different classes, to realize multilevel fault feature recognition and prediction of entire electric propulsion
systems with multiple faults and operational coupling.
Therefore, we analyzed big data from cruise operational maintenanceto obtain the correlation between different categories. Furthermore, we
identified and predicted the multi-level fault features of an overall
electric propulsion system with multiple fault and operational coupling
characteristics.

ABSTRACT
In order to reduce the influence of uncertainty due to fault of the main
propulsion equipment during cruise operation, we studied the correlationbetween fault features of the cruise main propulsion system. First, weexplored the quantization problem between equipment degradation andmonitoring parameters under multiple failure modes. According to thecorrelation degree, we established a weight matrix to find the hidden
correlation under the association rules. Then, we proposed a weighted
random forest algorithm. Through weighted voting, we analyzed andpredicted the fault features of the equipment in the main propulsion
system. Multi-level monitoring and fault diagnosis characterized bymulti-fault and operation coupling was implemented in the whole main
propulsion system. The experimental results show that based on theweight matrix and after considering the relevance, the classification
learning algorithm can find the trend of equipment degradation in timeand predict some faults in advance. Further, the fault feature recognition
accuracy is effectively improved by data preprocessing and algorithm
optimization, thereby providing reference and basis for reliability
analysis and risk prediction of the equipment’s whole life cycle.

KEY WORDS: Association analysis; Random forest; Fault
prediction; Cruise maintenance; Propulsion system

INTRODUCTION

Relevant theories and technical routes
In power system fault prediction studies, multi-depth neural networks
were used to intelligently identify power equipment faults (Qu, 2021).
Although the multi-layer neural network model provided accurate
analysis, it could not clarify the analysis process. The algorithm decisionmaking basis in each prediction was revealed by a decision tree modelthrough a series of branch sequences. In addition, the variable selection
criteria based on the decision tree model theory was helpful in judging
which variables played a significant role in model decision making,which factors resulted in failure, and whether any potential associations
could be established. However, the accuracy and performance of thedecision tree was poor when analyzing multiple or complex monitoring
variables, and was also prone to phenomena such as excessive branchingand over-fitting. An optimized random forest classification algorithm

In recent years, luxury cruise ships propelled by diesel-electric hybrid
systems accounted for 73.3% of all cruise ships (Li, 2018), and electric
propulsion systems almost accounted for half of all ships. Therefore,
cruise electric propulsion system operational maintenance safety andfault diagnosis have become increasingly important. In the field of data
analysis, it was believed that equipment failures were usually reflectedin the time-domain and frequency-domain (Gao, 2018), in varying
degrees, suggesting some internal connection between data changes andfailure decay. Therefore, if big data could quickly identify the beginningand evolution of faults on cruise ship electric propulsion equipment,
propulsion fault risks would be significantly reduced. This would reduce
economic losses, labor costs and time. In ship power propulsion
equipment fault analysis, a BP neural network was used by researchers3757