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Cerrada, M., Sánchez, R., Pacheco, F., Cabrera, D., Zurita, G. and Li, C. (2016) Hierarchical Feature Selection Based on Relative Dependency for Gear Fault Diagnosis. Applied Intelligence, 44, 687-703.
https://doi.org/10.1007/s10489-015-0725-3

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.