Chapter
Apr 26, 2012
Application of Fuzzy C-Mean Clustering Arithmetic in Ship Fault Diagnosis
Authors: Xinjie Han [email protected], Zuwen Wang, Xianyao Meng, and Song MengAuthor Affiliations
Publication: International Conference on Transportation Engineering 2007
Abstract
It is significant for a ship to diagnose and forecast the failures of facilities as early as possible. Because equipments in a ship are so many and the running conditions of them are so special, the traditional fault diagnosis methods are no more efficient in practice. Thus a new ship fault diagnosis method applying fuzzy C-mean clustering arithmetic is introduced in this paper. Based on the principle of it, we studied how to diagnose failures of an engine at the early time.
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© 2007 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Coasts, oceans, ports, and waterways engineering
- Computer programming
- Computing in civil engineering
- Diagnosis
- Engineering fundamentals
- Engines
- Equipment and machinery
- Failure analysis
- Forecasting
- Forensic engineering
- Fuzzy logic
- Mathematics
- Ships
- Statistics
- Structural engineering
Authors
Affiliations
Automation & Elec. Eng. College, Dalian Maritime Univ., Dalian 116026, China. E-mail: [email protected]
Zuwen Wang
Automation & Elec. Eng. College, Dalian Maritime Univ., Dalian 116026, China
Xianyao Meng
Automation & Elec. Eng. College, Dalian Maritime Univ., Dalian 116026, China
Song Meng
Automation & Elec. Eng. College, Dalian Maritime Univ., Dalian 116026, China
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.