Reliability and safety are major issues in tower crane applications. A new\nadaptive neurofuzzy system is developed in this work for real-time health\ncondition monitoring of tower cranes, especially for hoist gearboxes. Vibration\nsignals are measured using a wireless smart sensor system. Fault detection\nis performed gear-by-gear in the gearbox. A new diagnostic classifier is\nproposed to integrate strengths of several signal processing techniques for\nfault detection. A hybrid machine learning method is proposed to facilitate\nimplementation and improve training convergence. The effectiveness of the\ndeveloped monitoring system is verified by experimental tests.
Loading....