Jing, L., Zhao, M., Li, P. and Xu, X. (2017) A Convolutional Neural Network Based Feature Learning and Fault Diagnosis Method for the Condition Monitoring of Gearbox. Measurement, 111, 1-10.
https://doi.org/10.1016/j.measurement.2017.07.017

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.