محمدحسین هدایتی متین

Artificial intelligence, as one of the most advanced and transformative technologies of the ۲۱st century, has made a significant impact on various industries. This technology plays a fundamental role in various fields such as increasing productivity, reducing costs, improving quality, and increasing equipment reliability. Among the current challenges of industries, the high costs of machinery and the need for optimal utilization of assets in the maintenance and repair sector are increasing the need for the use of artificial intelligence in this field. Especially considering the increasing complexity of industrial systems and the need to increase efficiency, the use of intelligent and advanced methods in managing maintenance and repair processes is becoming a strategic need.

This research has addressed the analysis of artificial intelligence in the maintenance and repair of industrial equipment. For this purpose, ۲۷ scientific articles from the reputable Science Direct database that contained information and analysis related to artificial applications in this field were selected based on scientific indicators. From this article, ۷۵ conceptual analysis codes were extracted, which were categorized into five main themes: ۱) technical, ۲) economic-operational, ۳) data-infrastructure, ۴) organizational, and ۵) security-security-resilience. This >This research shows that the greatest impact of AI in maintenance is related to technical and operational outcomes, as AI significantly improves the processes of prediction, fault diagnosis, and optimization of maintenance operations. In addition, the economic and operational useful life, especially in the context of reducing operating costs and increasing equipment, has directly affected the efficiency and profitability of industries. Other themes also play supporting and developmental roles in the use of smart technologies, especially in the areas of data, security, and resilience of reservoir maintenance and repair systems. At the national level, given the country’s economic and industrial conditions, the use of smart technologies, especially artificial intelligence, in asset management and optimization of maintenance and repair processes is considered vital. This is of particular importance in important industries, especially given the challenges of increasing investment costs, equipment depreciation, and the country’s economic pressures. Developing data structures, training specialized human resources, and investing in this area can increase productivity, reduce maintenance costs, and increase the useful life of equipment in various industries in the country. These developments can be considered as focused steps in moving towards a sustainable, competitive industry and the use of technology in the country.

Keywords: Artificial intelligence, maintenance and repairs, predictive maintenance, content analysis, asset management

Abstract

Artificial intelligence, as one of the transformative industrial technologies, plays a key role in increasing productivity, reducing costs, and improving equipment reliability. Given the growth of machinery costs and the need to exploit assets, the use of artificial intelligence in maintenance and repair is a strategic necessity. In this study, ۲۷ articles from the Science Direct database were selected and reviewed based on scientific indicators through content analysis. From these articles, ۷۵ conceptual analysis codes were >At the national level, given the increasing investment costs and depreciation of equipment in the country's industries, the use of smart technologies, especially artificial intelligence, in asset management is an essential measure to increase productivity, reduce maintenance costs, and increase the useful life of equipment. The development of data infrastructure, human resource training, and targeted investment in this area can be a step towards moving towards a sustainable and competitive industry for technology in the country.

Keywords: Artificial intelligence, maintenance and repairs, predictive maintenance, content analysis, asset management