sayyed shahram fatemi

In this research, the intelligent model of preventive maintenance and repairs based on artificial neural network methodology - fuzzy logic with the help of artificial intelligence environment of MATLAB software based on the structure of Falcon's five-layer model of artificial neural networks is presented, the research method is based on systems thinking. After determining the most important factors affecting preventive maintenance and repairs with the help of a questionnaire and based on a dataset of 2,000 samples of data and reports of the Director General of Textile and Clothing Industries of the Ministry of Safety during the years 1396 to 1401 (in the form of six and a half years) and validity Data evaluation by the maintenance and repair experts of 240 industrial units, a smart model was designed, which after the implementation of the model in Borujerd textile factories as the place of implementation of the plan can be claimed if (If); Five "technology" factors have values of 0.9129; Good condition (upper bound of good membership function), "Employees" has values of 0.9239; good condition (upper bound of good membership function), "working environment" has values of 0.8859; relatively good (lower limit of the membership function), "quality" has values of 0.9999; Perfect condition (highest function), "strategy" has values of 0.9999; good status (upper limit) in preventive maintenance and repairs, then: the status of the output variable of the research, i.e."Optimization of preventive maintenance and repairs performance (Y)" will be at its fifth level, i.e. very good, equal to 0.882.

In this study, the identification of the most important influencing factors for intelligent preventive maintenance and repair in the textile industry in interaction with manufacturing was performed using Spss 25 software. The research data set is based on the 2000 sample data and reports from the Director General of Textile Industry under the Ministry of Industry, Mine and Trade and the factories in the textile industry of Borujerd where the project was implemented on a semiannual basis as of 2015 to 2020.  This survey is significant in that it collects information in cooperation with specialists in the maintenance industry and the textile industry (information was collected from 240 establishments of 630 textile factories nationwide). The study sample size included professors, managers, and professionals from the textile industry. A comparison of the pre-test questionnaire (functional status) and the post-test questionnaire (ideal status) and statistical analysis calculations yielded the following results. The relationships between the study variables and the most important preventive maintenance and improvement factors in interaction with production in the textile industry have been classified as follows: strategy factor with the possibility of determining 34%, technology factor 30%, work environment factor 16%, staff factor 10%, the quality factor was identified by 10%.