GPT答：

7i24.cn

Propose an improved random forest algorithm based on cyclic sparsity and slime mould algorithm for fault diagnosis of gears. Firstly, a cyclic sparse decomposition model is established for envelope analysis, and the envelope harmonic-to-noise ratio is calculated. Then, the feature vector is constructed by combining the envelope harmonic-to-noise ratio with time-domain, frequency-domain and other indicators. The slime mould algorithm (SMA) is introduced to optimize two parameters of random forests (RF) for gear fault recognition. The performance of the proposed method is compared with three classification methods: RF, long short-term memory network (LSTM), and convolutional neural network (CNN). Experimental results show that the improved RF can improve classification accuracy.