NDLI: A Hybrid LSSVR/HMM-Based Prognostic Approach

Content Provider	PubMed Central
Author	Liu, Zhijuan Qing, Li Liu, Xianhui Mu, Chundi
Copyright Year	2013
Abstract	In a health management system, prognostics, which is an engineering discipline that predicts a system's future health, is an important aspect yet there is currently limited research in this field. In this paper, a hybrid approach for prognostics is proposed. The approach combines the least squares support vector regression (LSSVR) with the hidden Markov model (HMM). Features extracted from sensor signals are used to train HMMs, which represent different health levels. A LSSVR algorithm is used to predict the feature trends. The LSSVR training and prediction algorithms are modified by adding new data and deleting old data and the probabilities of the predicted features for each HMM are calculated based on forward or backward algorithms. Based on these probabilities, one can determine a system's future health state and estimate the remaining useful life (RUL). To evaluate the proposed approach, a test was carried out using bearing vibration signals. Simulation results show that the LSSVR/HMM approach can forecast faults long before they occur and can predict the RUL. Therefore, the LSSVR/HMM approach is very promising in the field of prognostics.
Related Links	http://dx.doi.org/10.3390/s130505542
Ending Page	5560
Page Count	19
Starting Page	5542
File Format	PDF
ISSN	14248220
e-ISSN	14248220
Journal	Sensors (Basel, Switzerland)
Issue Number	5
Volume Number	13
Language	English
Publisher	Molecular Diversity Preservation International (MDPI)
Publisher Date	2013-04-26
Access Restriction	Open
Rights Holder	Molecular Diversity Preservation International (MDPI)
Subject Keyword	Sensors and actuators Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Medicine Atomic and Molecular Physics, and Optics Instrumentation Analytical Chemistry Biochemistry Information Systems Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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