Comparative Study on Health Monitoring of a Marine Engine Using Multivariate Physics-Based Models and Unsupervised Data-Driven Models
Author:
Fu Chao12ORCID, Liang Xiaoxia3, Li Qian4, Lu Kuan12ORCID, Gu Fengshou5ORCID, Ball Andrew D.5ORCID, Zheng Zhaoli6
Affiliation:
1. Collaborative Innovation Center of Northwestern Polytechnical University, Shanghai 201108, China 2. Institute of Vibration Engineering, Northwestern Polytechnical University, Xi’an 710072, China 3. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China 4. Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology, China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou 511370, China 5. Centre for Efficiency and Performance Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK 6. Science and Technology on Thermal Energy and Power Laboratory, Wuhan Second Ship Design and Research Institute, Wuhan 430205, China
Abstract
The marine engine is a complex-structured multidisciplinary system that operates in a harsh environment involving high temperatures and pressures and gas/fluid/solid interactions. Many malfunctions and faults can occur to the marine engine and efficient condition monitoring is critical to ensure the expected performance. In this paper, a marine engine test rig is established and its process data are recorded, including various temperatures and pressures. Two data-driven models, i.e., principal component analysis and the sparse autoencoder, and a physics-based model are applied to the marine engine for two classic faults, i.e., lubrication oil filter blocking and cylinder leakage. Comparative studies and discussions are conducted regarding their performance in terms of anomaly detection and fault isolation. The data points collected for the filter blocking fault are generally two times higher than the fault thresholds set by the data-driven models. In the physics-based model, it is observed that the lubrication oil pressure falls from the predicted 3.2–3.8 bar to around 2.3 bar. For the cylinder leakage fault, the fault test data are nearly four times higher than the thresholds in the data-driven models. The exhaust gas temperature of the leaked cylinder falls from an estimated 150–200 °C to about 100 °C. The transferability and interpretability of these models are finally discussed. The findings of the present study offer insights into the two types of models and can provide guidance for the effective condition monitoring of marine engines.
Funder
Shanghai Sailing Program
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering
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