船舶机械设备故障诊断联合实验室建设的探索与实践
Exploration and Practice of Building a Joint Laboratory for Fault Diagnosis of Ships Machinery Equipment
摘要: 针对轮机工程专业船舶机械设备故障诊断实验室建设中存在的信息交流机制不健全、实验资源共享不深入等问题，构建“校内校外双循环”的建设框架，依托“学校–工厂–研究所–船舶”四方协同的共享平台，建立“监测诊断闭环反馈”机制和“联教联训联研联保”模式。该方案通过整合跨领域资源、创新人才培养流程，为机组健康监测智能运维新型人才培养和监测诊断新技术研发推广提供支撑，对深化产学研融合、提升轮机工程专业教学质量具有实践意义。
Abstract: In response to the problems of incomplete information exchange mechanisms and insufficient sharing of experimental resources in the construction of the marine machinery equipment fault diagnosis laboratory in the field of marine engineering, a construction framework of “dual circulation within and outside the school” is proposed. Based on the shared platform of “school factory research institute ship” four-party collaboration, a “monitoring and diagnosis closed-loop feedback mechanism” and a “joint education, joint training, joint research and joint guarantee” model are established. This plan integrates cross-disciplinary resources and innovates the talent training process, providing support for the cultivation of new talents in intelligent operation and maintenance of unit health monitoring and the research and promotion of new monitoring and diagnostic technologies. It has practical significance for deepening the integration of industry, academia, and research and improving the teaching quality of marine engineering.
文章引用：杨云生, 柴凯, 蔡博奥, 刘树勇. 船舶机械设备故障诊断联合实验室建设的探索与实践[J]. 服务科学和管理, 2025, 14(6): 799-807. https://doi.org/10.12677/ssem.2025.146099

参考文献

[1] 教育部. 高校国际合作联合实验室建设与管理办法[EB/OL]. 2014-07-18.
http://www.moe.gov.cn/srcsite/A16/kjs_gjhz/201407/t20140718_172350.html, 2025-01-16.
[2] Cheliotis, M., Lazakis, I. and Theotokatos, G. (2020) Machine Learning and Data-Driven Fault Detection for Ship Systems Operations. Ocean Engineering, 216, Article 107968. [Google Scholar] [CrossRef] 
[3] Tan, Y., Zhang, J., Tian, H., Jiang, D., Guo, L., Wang, G., et al. (2021) Multi-Label Classification for Simultaneous Fault Diagnosis of Marine Machinery: A Comparative Study. Ocean Engineering, 239, Article 109723. [Google Scholar] [CrossRef] 
[4] Etzkowitz, H. and Leydesdorff, L. (2000) The Dynamics of Innovation: From National Systems and “Mode 2” to a Triple Helix of University-Industry-Government Relations. Research Policy, 29, 109-123. [Google Scholar] [CrossRef] 
[5] Clark, B.R. (1998) Creating Entrepreneurial Universities: Organizational Pathways of Transformation. Pergamon Press.
[6] Powell, W.W., Koput, K.W. and Smith-Doerr, L. (1996) Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology. Administrative Science Quarterly, 41, 116-145. [Google Scholar] [CrossRef] 
[7] Lazakis, I., Raptodimos, Y. and Varelas, T. (2018) Predicting Ship Machinery System Condition through Analytical Reliability Tools and Artificial Neural Networks. Ocean Engineering, 152, 404-415. [Google Scholar] [CrossRef] 
[8] Xu S, Liu H, Bi F, et al. (2022) Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy. Machines, 10, Article 72. [Google Scholar] [CrossRef] 
[9] 王巨宏, 刘婷婷. 新工科背景下产学合作协同育人模式探索与实践[J]. 高等工程教育研究, 2021(3): 45-52.
[10] Kim, J., Lee, T., Lee, S., Lee, J., Lee, W., Kim, Y., et al. (2022) A Study on Deep Learning-Based Fault Diagnosis and Classification for Marine Engine System Auxiliary Equipment. Processes, 10, Article 1345. [Google Scholar] [CrossRef] 
[11] 张立军, 王晓明, 陈云峰. 高校联合实验室资源共享机制研究[J]. 实验技术与管理, 2020, 37(8): 235-238.
[12] Olson, A. and Manohar, A. (2024) Recent PhD Graduates Use AI to Predict Ship Failures at the Marine Engineering Lab. University of Michigan.
[13] 李明, 赵华, 陈强. 校企合作促进科技资源整合的模式与路径研究[J]. 中国高校科技, 2019(12): 78-81.
[14] Korczewski, Z. (2016) Exhaust Gas Temperature Measurements in Diagnostics of Turbocharged Marine Internal Combustion Engines Part II Dynamic Measurements. Polish Maritime Research, 23, 68-76. [Google Scholar] [CrossRef] 
[15] Det Norske Veritas Germanischer Lloyd (2020) Condition Based Maintenance (CBM)-Rules and Standards. DNVGL-RU-OU-0300.

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