Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural Network

Abstract

To address difficult recognition of vibration signals of heavy-load robots, a new median neural network pattern recognition method based on artificial neural network was proposed. This method builds the pattern recognition network model by using the median neurons and particle swarm optimization algorithm. In other words, the fault modes of heavy-load industrial robots are recognized quickly and accurately by using median and multiplication function as the aggregate function, and combining with the particle swarm backpropagation algorithm under the premise that there’s abnormal or fault eigenvalues in the network input mode. According to the simulation analysis and case verification, the proposed median neural network method can recognize fault modes of heavy-load robots well and it still and a smaller one recognition fault rate more traditional algorithms In below the complicated severe environment with noises. It shows remarkable recognition accuracy as well as relatively high compatibility and stability. This method not only has high theoretical value in pattern recognition of heavy-duty robots, but also possesses very important engineering practice significance in fault pattern recognition of rotary machines like ventilator and gas blower.The proposed method not only has high theoretical value in pattern recognition of heavy-duty robots, but there are also important,it has practical significance in engineering and other fields fault pattern recognition of rotating machinery such as ventilators and gas blowers.