NDLI: Detection and Diagnosis of Incipient Faults in Heavy-Duty Diesel Engines

Content Provider	IEEE Xplore Digital Library
Author	Morgan, I. Honghai Liu Tormos, B. Sala, A.
Copyright Year	1982
Abstract	This paper proposes a new methodology for detecting and diagnosing faults found in heavy-duty diesel engines based upon spectrometric analysis of lubrication samples and is compared against a conventional method, the redline limits, which is utilized in a number of major laboratories in the U.K. and across Europe. The proposed method applies computational power to a well-known maintenance technique and consists of an improved method of preprocessing to form a derivative tuple, which extracts further information from the measured elemental concentrations. To identify incipient faults, the distance in vector space is calculated using a Gaussian contour, generated from prior data, as the zero crossing, which enables novel samples to be classified as normal or abnormal. This information is utilized as the input to a probabilistic directed acyclic graph in the form of a belief network. This network provides a prognosis for the mechanism as well as suggesting possible actions that could be taken to rectify the diagnosed problem, supported with confidence probabilities. The proposed method is evaluated for both accuracy in detecting a fault as well as the duration of time that is provided before the event occurs, with significant improvements in both metrics demonstrated over the conventional method.
Sponsorship	IEEE Industrial Electronics Society
Starting Page	3522
Ending Page	3532
Page Count	11
File Size	698176
File Format	PDF
ISSN	02780046
Volume Number	57
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault detection Fault diagnosis Diesel engines Spectroscopy Lubrication Laboratories Europe Data mining Power measurement Event detection spectrometry Bayesian belief network diagnosis diesel engines fault detection Gaussian incipient faults one-class classification
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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