Author(s)

Aaron K. Adik¹, Wilson Wang^2*

¹Department of Electrical and Computer Engineering, Lakehead University, Thunder Bay, Canada.
²Department of Mechanical Engineering, Lakehead University, Thunder Bay, Canada.

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.

Adaptive Neuro-Fuzzy Systems, Machine Learning, Diagnostics, Pattern Classification, Tower Cranes, Smart Sensors

Adik, A. and Wang, W. (2019) An Intelligent System for Real-Time Condition Monitoring of Tower Cranes. Intelligent Control and Automation, 10, 155-167. doi: 10.4236/ica.2019.104011.