基于FSA-ACO混合改进算法的蜗轮蜗杆故障识别

Worm Gear Fault Identification Based on FSA-ACO Mixed Improved Algorithm

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。

A new method for fault identification of worm algorithm -ant colony optimization） algorithm is proposed. The gies to optimize the relevant parameters of the algorithm in gears based on mixed improved FSA-ACO （fish swarm method first proposes mixed improved FSA-ACO strateseeking constructing the algorithm model that combines this algorithm a complementary of advantages. Meanwhile, in 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.