Intelligent reliability management model for operated agricultural machinery

Agricultural machinery, as the primary production asset of an enterprise, requires a specialized information management system. Managing the reliability of operated agricultural machinery presents a relevant and complex challenge. The low level of implementation of decision support tools, combined with subjective maintenance and repair management that fails to account for objective control factors and data, leads to ineffective management decisions. The study aimed to develop an intelligent reliability management model for operated agricultural machinery based on the principles of a process approach. The model takes into account the permissible level of failure-free operation probability (risk), maintainability, operating conditions, and cost of losses, while minimizing the involvement of the engineer in management decision-making. The developed model is founded on an artificial intelligence management system incorporating a set of core algorithms, integrated with remote data collection tools for assessing the technical condition of machine components. An algorithm has been developed for autonomously making and editing maintenance schedules, taking into consideration both technical-economic and subjective criteria. The implementation of the proposed automated intelligent management model is expected to reduce the time specialists spend on routine management tasks by 40% and to replace human involvement in at least half of the management decisions related to maintaining machine operability. The developed intelligent management model can be applied to tractors, harvesting combines, and mounted and trailed agricultural machines. It will enable the implementation of lean reliability management technology for machinery and reflects the core concept and operating principle of a digital twin of a machine operation engineer.

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