Predictive maintenance of construction machinery based on neural network analysis of sensor data: reduced downtime and increased service life
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Abstract
This study is devoted to the development and implementation of a predictive maintenance system for construction equipment based on neural network analysis of sensor data. The use of neural networks for processing and interpreting multidimensional data from various sensors makes it possible to accurately predict the technical condition of equipment and the need for repairs. The paper presents a comprehensive methodology that integrates modern machine learning approaches and Internet of Things technologies to monitor critical parameters of construction machinery in real time. The empirical base of the study includes data from vibration, temperature and acoustic sensors collected from the fleet of construction equipment of a large infrastructure project during 18 months of operation. The developed system has demonstrated the ability to reduce unplanned equipment downtime by 47.3%, extend the service life of key components by 32.6%, and reduce overall maintenance costs by 28.5%. The results confirm that the integration of neural network algorithms into the maintenance management system makes it possible to optimize the operational characteristics of construction equipment and significantly improve the efficiency of educational construction project management through the rational use of resources and minimizing the risks of unplanned downtime.
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