Affiliations: [a] Department of MCT, U.E.T., Lahore, Pakistan | [b] Department of CS & E, U.E.T., Lahore, Pakistan | [c] Department of CS, CINVESTAV-IPN, D.F., Mexico | [d] Artificial Intelligence Research Institute, IIIA-CSIC, Barcelona, Spain
Correspondence:
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Corresponding author: Aslam Muhammad, Department of CS & E, U.E.T., Lahore, P. Code 54567; Pakistan. Tel.: +92 42 9029260; Fax: +92 42 9250202; E-mail: [email protected]
Note: [1] This letter is given on April 27th, 2011 at Mexico City.
Abstract: Reliability centered maintenance provides failure free functionality of heavy plant equipment. Tedious preventive maintenance tasks, inapplicable solutions and significant start up costs are the major limitations of this strategy. The aim of this paper is to improve the analysis efficiency of reliability centered maintenance without trading off quality by its integration with artificial intelligence. In order to achieve key performance indicators for the plant management, we incorporated rule based reasoning along with the case based reasoning. While only case based technique was introduced previously, our approach reduces repetitive tasks and also provides support in predicting equipment needs, by applying artificial neural networks.
