FFDEZOA Optimized Clustering Analysis of SCARA Robot Fault Data
A FFDEZOA algorithm is proposed to optimize the KMC clustering algorithm in response to the problems of high dependence on initial point selection,slow convergence speed,and low accuracy in existing clustering methods for robot fault data clustering.The ZOA algorithm has strong optimization a-bility,fast convergence speed,and little dependence on initial point selection during clustering,but it has a chance of falling into local optimal solutions.Firstly,in response to the shortcomings of the ZOA algo-rithm,free foraging strategy,nonlinear convergence factor,and zebra evolution strategy were proposed to improve it,which can effectively increase the search range of the algorithm and avoid local optima;further-more,combining the complementary iteration of FFDEZOA and KMC algorithms not only accelerates the search speed of the algorithm,but also improves accuracy.Experiments on multiple public datasets have shown that FFDEZOA-KMC outperforms ZOA-KMC,AO-KMC,KMC,and MFO-KMC in terms of accuracy and normalized mutual information,with better convergence performance and clustering perform-ance.Finally,based on the different principal components of each fault feature,FFDEZOA-KMC was used to cluster the fault data,which can provide targeted maintenance and upkeep for robots under various working conditions.
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