A new remote intelligent diagnosis system for marine diesel engines based on an improved multi-kernel algorithm

Yupeng Yuan, Xinping Yan, Kai Wang and Chengqing Yuan

Journal of Risk and Reliability, 2015, vol. 229, issue 6, 604-611

Abstract: Due to heavy work load of marine diesel engines, the failure in their mechanical components may result in serious accidents. Existing condition monitoring methods for marine diesel engines usually adopt warning after the failure occurred. In order to predict potential faults, this work has put forward a remote intelligent monitoring system for marine diesel engines. The global system for mobile communication mode was employed to construct the basis of data remote transmission, and a new multi-kernel extreme learning machine algorithm was proposed to diagnose the early faults in an intelligent method. Experimental tests were carried out in the marine diesel engine fault diagnosis set-up. The analysis results show that the proposed remote intelligent monitoring system can accurately, timely and reliably detect the potential failures. Meanwhile, the proposed multi-kernel extreme learning machine was compared with the existing methods. The comparison indicates that the multi-kernel extreme learning machine outperforms its rivals in term of fault detection rate by an increase of 3.4%. Therefore, the proposed remote intelligent monitoring system has good prospects for engineering applications.

Keywords: Marine diesel engines; remote communication; fault diagnosis; extreme learning machine; multi-kernel algorithm (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1748006X15595541 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:229:y:2015:i:6:p:604-611

DOI: 10.1177/1748006X15595541

Access Statistics for this article

More articles in Journal of Risk and Reliability
Bibliographic data for series maintained by SAGE Publications ().