TITLE:
An Intelligent System for Real-Time Condition Monitoring of Tower Cranes
AUTHORS:
Aaron K. Adik, Wilson Wang
KEYWORDS:
Adaptive Neuro-Fuzzy Systems, Machine Learning, Diagnostics, Pattern Classification, Tower Cranes, Smart Sensors
JOURNAL NAME:
Intelligent Control and Automation,
Vol.10 No.4,
November
26,
2019
ABSTRACT: Reliability and safety are major issues in tower crane applications. A new adaptive neurofuzzy system is developed in this work for real-time health condition monitoring of tower cranes, especially for hoist gearboxes. Vibration signals are measured using a wireless smart sensor system. Fault detection is performed gear-by-gear in the gearbox. A new diagnostic classifier is proposed to integrate strengths of several signal processing techniques for fault detection. A hybrid machine learning method is proposed to facilitate implementation and improve training convergence. The effectiveness of the developed monitoring system is verified by experimental tests.