Extracting knowledge of preventive maintenance using data mining technique in interaction with production within textile industry

In the current research, the dataset for conducting data mining calculations was generated based on a sample with ۲,۰۰۰ data, reports of the general manager of the textile industry of Iran's Ministry of Industry, Mine and Trade (information from ۲۴۰ industrial units and ۶۳۰ spinning and weaving units were collected), and textile industry plants in Borujerd as the place for implementing the plan between ۲۰۱۵ and ۲۰۱۹, a period ۶ month each year. Due to extensive information from the textile industry (with the help of the Ministry of Industry, Mine and Trade), the current research is unique. Using IBM SPSS Modeler ۱۸, the most significant results of datamining calculations to extract knowledge are as follows, which are arranged based on main predictors of the research: predicting models of "strategy innovation in net with data code (A۵)" with the prediction wight of ۰.۳۴; "technology innovation in net with data code (A۱)" with the prediction wight of ۰.۳۰; "work environment innovation in net with data code (A۳)" with the prediction wight of ۰.۱۶; Quality innovation in net with data code (A۴)" with the prediction wight of ۰.۱۵; "employe  innovation in net with data code (A۲)" with the prediction wight of ۰.۱۰ are utilized to accurately analyze preventive maintenance in interaction with production.

Keywords:

Preventive Maintenance Systems , Data mining , IBM MODELER , textile industry