Research On Fault Diagnosis Of Medium-speed Maglev Operation Control Equipment Based On Glowworm Swarm Optimization Clustering

Operation control equipment is the core equipment to ensure the safety protection and automatic control of the medium-speed maglev train.Due to the aging of internal modules and the effects of external environmental factors,fault occurrence in the operation control equipment is unavoidable.Therefore,the fault diagnosis research on the operation control equipment is of considerable significance to ensuring fail-safe of medium-speed maglev during the whole operation process.In this paper,a new method for fault diagnosis of medium-speed maglev operation control equipment based on glowworm swarm optimization clustering is proposed by studying the existing fault diagnosis methods of train operation control equipment.Then,combined with big data technology,intelligent fault diagnosis of medium-speed maglev operation control equipment is realized.The specific contents are as follows:(1)Two improved Glowworm Swarm Optimization(GSO)algorithms are proposed.Aiming at mitigating the slow convergence and low precision of the GSO algorithm in the late stage of dealing with complex multi-modal optimization problems,the variable step-size glowworm swarm optimization(VSGSO)algorithm is proposed.Based on the VSGSO algorithm,the variable step-size and updating search domains glowworm swarm optimization(VSGSO-D)algorithm is proposed by improving the location update strategies and search domains of the glowworm.The optimal initiation parameters combination of the improved algorithms that reduce the difficulty of the algorithms are obtained through the algorithm parameters selection experiment.The comparison experiments show that the proposed VSGSO-D algorithm has better global convergence speed,optimization precision,and stability in the optimization of multi-modal function.By protecting the diversity of the glowworm population,the multi-local optimization ability of the algorithm is improved.(2)A fault clustering algorithm based on VSGSO-D for medium-speed maglev operation control equipment is proposed.The failure modes of the main operation control equipment of medium-speed maglev are analyzed.Then the relationship between the failure modes of the critical operation control equipment and the monitoring parameters is established.Combing the multi-modal optimization ability of VSGSO-D algorithm,a VSGSO-D self-organizing clustering algorithm is proposed.The experimental results show that the algorithm effectively realize the self-organizing clustering of data without initializing the cluster centers and cluster number.On this basis,a VSGSO-D hybrid clustering algorithm is proposed by combing the above clustering algorithm and the k-means algorithm.Finally,the fault clustering comparison experiments of k-meansik-means++,k-means,and the proposed VSGSO-D + k-means hybrid cluster:ing algorithm are conducted.The results show that the hybrid algorithm proposed in this paper has better clustering quality:that is,the proposed algorithm can better realize the fault clustering of medium-speed maglev operation control equipment.(3)A fault diagnosis model of medium-speed maglev operation control equipment based on VSGSO-D hybrid clustering algorithm and a distributed big data storage and analysis system are constructed,which realize the fault diagnosis of medium-speed maglev critical operation control equipment and has obtained a relatively high accuracy of fault diagnosis.