基于FSA ACO混合改进算法的蜗轮蜗杆故障识别

针对蜗轮蜗杆故障诊断问题，提出基于FSA ACO混合改进算法的蜗轮蜗杆故障识别的研究方法。该方法提出了FSA ACO混合改进策略，在谋求一个优势互补的基础上，对算法相关参数优化。同时针对该算法与蜗轮蜗杆故障识别结合构建算法模型问题，提出利用近邻函数准则作理论桥梁策略，寻找一种新的基于FSA ACO混合算法的蜗轮蜗杆故障诊断技术研究方法。以WPA40型号的蜗轮蜗杆为测试对象，验证了该研究方法的可行性和有效性。

Worm Gear Fault Identification Based on FSA ACO Mixed Improved Algorithm

A new method for fault identification of worm gears based on mixed improved FSA ACO (fish swarm algorithm ant colony optimization) algorithm is proposed. The method first proposes mixed improved FSA ACO strategies to optimize the relevant parameters of the algorithm in seeking a complementary of advantages. Meanwhile, in constructing the algorithm model that combines this algorithm with fault identification for worm gears, a strategy guideline based on the neighbor function theory is proposed, looking for a new fault diagnosis technology for worm gears based on mixed FSA ACO algorithm. Worm gears of WPA40 are taken as the test model to testify the feasibility and effectiveness of the research method.