Equipment Fault Prognosis Based on Temporal Association Rules

Chao GAN, Yuan LU, Ying HU, Jia GU, Xin QIU

Abstract


Equipment fault prognosis is important for reliability, operational safety, and efficient performance of equipment. Temporal fault data model is built according to the principles of the Apriori traditional association rules algorithm based on the characteristics of fault data. An Improved Apriori algorithm and frequent temporal association rules algorithm are proposed in this study by converting fault data to temporal item sets matrix. Equipment fault trends are predicted by mining the frequent temporal association rules of fault data based on the algorithm, which provides good support for equipment maintenance and management. At last an example is given to prove the feasibility and practical application of proposed algorithms

 

DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4563


Keywords


Fault Prognosis; Temporal Association Rules; Apriori algorithm; Data Mining ;Frequent Item sets;

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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