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An Intelligent System for Real-Time Condition Monitoring of Tower Cranes
Department of Electrical and Computer Engineering, Lakehead University, Thunder Bay, Canada
Department of Mechanical Engineering, Lakehead University, Thunder Bay, Canada
- 1 Department of Electrical and Computer Engineering, Lakehead University, Thunder Bay, Canada
- 2 Department of Mechanical Engineering, Lakehead University, Thunder Bay, Canada
Intelligent Control and Automation·Volume 10 (2019)·Pages 155–167·Published 16 October 2019·DOI10.4236/ica.2019.104011
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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.
KeywordsAdaptive Neuro-Fuzzy SystemsMachine LearningDiagnosticsPattern ClassificationTower CranesSmart Sensors
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