Rapid localization and predictive monitoring of bolt loosening in complex electromechanical systems: Smart washer system based on neural networks

Abstract

Bolt connections are critical components in combine harvesters, yet they are prone to loosening and failure due to prolonged cyclic vibrations and impact loads. To address the inefficiency of manual inspections, this study proposes a rapid detection method and an intelligent monitoring system for bolt groups. First, a novel detection method based on piezoresistive sensors and a series resistance circuit is introduced, utilizing a unique resistance encoding strategy and a regional binary search mechanism. Second, to enable predictive maintenance, a feedforward neural network model is developed to forecast bolt pressure trends based on historical data. Furthermore, an intelligent monitoring washer is designed, featuring LoRa wireless communication and integrated miniature solar panels. Experimental evaluations show that this approach improves detection efficiency by up to 91.7%-operationalized as the reduction in required inspection steps-compared to traditional sequential methods. The developed feedforward neural network achieved prediction errors within 5%. Finally, the entire system is integrated into a Python-based visual platform for real-time data acquisition, trend analysis, and loosening warnings. This research provides a robust engineering solution for the online monitoring and health management of agricultural machinery.