基于确定学习的机器人故障诊断

针对存在扰动的机器人系统,提出了一种基于确定学习的故障诊断方法.基于确定学习理论,采用RBF神经网络获取正常模式和各种故障模式下的系统动态知识,并将学到的知识以常数神经网络权值的形式存储于模式库中.诊断过程不再需要重新训练神经网络,而是对已学知识的再利用,并运用动态模式识别的方法实现故障的快速检测与分离.仿真验证了所提方法的正确性和有效性. 

Fault Diagnosis of Robot Based on Deterministic Learning

A rapid fault diagnosis scheme based on deterministic learning was proposed for robot with disturbance. The system dynamics underlying normal and various fault modes were locally accurately approximated through deterministic learning, and the obtained knowledge of system dynamics was stored as constant neural weights to build out a mode bank. In the diagnostic process, the learned knowledge was reused through the dynamic pattern recognition method so that the fault can be detected and isolated quickly without training neural networks again. Simulations were included to demonstrate the effectiveness of the proposed approach.