Design and optimization of wireless sensor system for self-powered crawler crane based on piezoelectric energy harvesting
Year: 2026
Type: article
Abstract: Introduction Crawler cranes are widely used in large–scale infrastructure construction, where structural health monitoring is essential to ensure operational safety. Wireless sensor networks have become a mainstream solution for crane monitoring; however, conventional battery–powered systems suffer from frequent replacement, complex wiring, and limited service life, which restrict long–term deployment. Methods To address these issues, a piezoelectric energy harvesting electromechanical coupling model tailored to crawler crane operating conditions is developed. Furthermore, a low–power wireless communication protocol incorporating cluster–head data aggregation and dynamic duty–cycle adjustment is introduced, enabling deep collaboration between energy harvesting, energy storage, and wireless sensing modules. Results Simulation results show that within the resonance frequency range of 30–35 Hz, the optimized piezoelectric energy harvesting module achieves a peak output power of 8.0 mW at an acceleration of 0.5 g, representing a 47.5% improvement over the unoptimized configuration. Under the same excitation level, the energy storage capacitor voltage increases to 3.0 V within 25 s. Field deployment experiments involving six sensor nodes demonstrate that the proposed joint optimization scheme attains an energy utilization rate of 81.5%, while extending the average node lifetime to 397.4 h, which is 65.6% longer than that of the unoptimized scheme. Discussion This study proposes a “structure–circuit–communication” collaborative optimization framework for complex vibration environments of crawler cranes. The proposed approach enables long‐term online monitoring of wireless sensor nodes without batteries and provides a feasible technical pathway for upgrading self-powered Internet of Things systems in large‐scale construction machinery.
Authors: Yufang Sun, Huiqian Liu
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,418
OpenAlex ID: W7122759045
