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Qu, Y., He, D., Yoon, J., Hecke, B., Bechhoefer, E. and Zhu, J. (2017) Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors—A Comparative Study. Archives of Computational Methods in Engineering, 24, 543-556.

has been cited by the following article:

• 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.