+44 (0)1509 263171
Please use this identifier to cite or link to this item:
|Title: ||A methodology for developing local smart diagnostic models using expert knowledge|
|Authors: ||Madsen, Anders L.|
Sayed, Mohamed S.
|Issue Date: ||2015|
|Publisher: ||© IEEE|
|Citation: ||MADSEN, A.L. ...et al., 2015. A methodology for developing local smart diagnostic models using expert knowledge. IN: 2015 13th IEEE International Conference on Industrial Informatics, (INDIN 2015), Cambridge, 22-24th July, pp. 1682-1687.|
|Abstract: ||© 2015 IEEE. This paper describes an innovative modular component-based modelling approach for diagnostics and condition-monitoring of manufacturing equipment. The approach is based on the use of object-oriented Bayesian networks, which supports a natural decomposition of a large and complex system into a set of less complex components. The methodology consists of six steps supporting the development process: Begin, Design, Implement, Test, Analyse, and Deploy. The process is iterative and the steps should be repeated until a satisfactory model has been achieved. The paper describes the details of the methodology as well as illustrates the use of the component-based modelling approach on a linear axis used in manufacturing. This application demonstrates the power and flexibility of the approach for diagnostics and condition-monitoring and shows a significant potential of the approach for modular component-based modelling in manufacturing and other domains.|
|Description: ||Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Version: ||Accepted for publication|
|Publisher Link: ||http://dx.doi.org/10.1109/INDIN.2015.7281987|
|Appears in Collections:||Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)|
Files associated with this item:
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.