Abstract
Aiming at problems in detecting mechanic equipment failure like large spatial dimension of data and inaccurate failure effect, etc., this paper proposes a failure detection model (AFSA-SVM) combining artificial fish swarm algorithm (AFSA) and supporting vector machine. Firstly, encode subset of network features into location of artificial fish while taking detection rate of 50% cross verification SVM training model as the standard to evaluate the featured subset, and then find the optimal subset through simulating foraging, clustering and following behaviors of fish swarm. Finally, SVM detects failures according to the optimal featured subset, and show through specific experimental data that algorithm in this paper simplifies the neutral network structure and improves speed of detecting failures while guaranteeing the accuracy of detecting failures.
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