An approximate algorithm for prognostic modelling using condition monitoring information

Established condition based maintenance modelling techniques can be computationally expensive. In this paper we propose an approximate methodology using extended Kalman-filtering and condition monitoring information to recursively establish a conditional probability density function for the residual...

Full description

Saved in:
Bibliographic Details
Published in:European journal of operational research Vol. 211; no. 1; pp. 90 - 96
Main Authors: Carr, Matthew J., Wang, Wenbin
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 16.05.2011
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Applied sciences
Approximation
Comparative analysis
Condition based maintenance
Condition based maintenance Extended Kalman filter Condition monitoring Prognostic modelling Residual life
Condition monitoring
Decision making models
Decision theory. Utility theory
Exact sciences and technology
Extended Kalman filter
Industrial metrology. Testing
Information
Kalman filters
Maintenance
Mathematical analysis
Mathematical models
Mechanical engineering. Machine design
Methodology
Modelling
Operational research and scientific management
Operational research. Management science
Prognostic modelling
Reliability theory. Replacement problems
Residual life
Studies
Condition monitoring
EKF
MLE
CM
RL
AIC
Condition based maintenance
Residual life
Extended Kalman filter
Prognostic modelling
CBM
Script
Data analysis
Automation
On line
Vibration
Kalman filter
Replacement problem
Durability
Inference
Maintenance
Conditional probability
Modeling
Multidimensional analysis
Surveillance
Probability density function
Kalman filtering
Monitoring
ISSN:0377-2217, 1872-6860
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Established condition based maintenance modelling techniques can be computationally expensive. In this paper we propose an approximate methodology using extended Kalman-filtering and condition monitoring information to recursively establish a conditional probability density function for the residual life of a component. The conditional density is then used in the construction of a maintenance/replacement decision model. The advantages of the methodology, when compared with alternative approaches, are the direct use of the often multi-dimensional condition monitoring data and the on-line automation opportunity provided by the computational efficiency of the model that potentially enables the simultaneous condition monitoring and associated inference for a large number of components and monitored variables. The methodology is applied to a vibration monitoring scenario and compared with alternative models using the case data.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2010.10.023