A new elite opposite sparrow search algorithm-based optimized LightGBM approach for fault diagnosis

In this paper, a new fault diagnosis approach based on elite opposite sparrow search algorithm (EOSSA) optimized LightGBM is proposed. It is necessary to extract appropriate features when dealing with high-dimensional data. Since the distribution of the high-dimensional data is not always approximately subject to a normal distribution, it will cause errors when it is approximated to normal distribution for feature extraction. The dimension reduction algorithms based on Euclidean distance often ignore the change of data distribution. To address this problem, cam locally linear discriminate embedding (CLLDE) based on cam weighted distance is proposed, which can improve the performance dealing with the deformed data of locally linear discriminate embedding (LLDE). The performance of CLLDE is better than LLDE on the iris dataset. It is important to establish a classifier with optimized hyper-parameters for fault identification. Sparrow search algorithm (SSA) is a novel optimization algorithm, which has achieved good results in many applications, but its optimization ability and convergence speed still need to be improved. Elite opposite sparrow search algorithm (EOSSA) is proposed by introducing elite opposite learning strategy and orifice imaging opposite learning strategy into SSA. The optimization results on benchmark functions show that EOSSA converges faster and has better optimization ability compared with the other five algorithms. EOSSA is used to optimize the hyper-parameters of LightGBM to train a classifier that can obtain a better fault recognition rate. Finally, the effectiveness of the proposed fault diagnosis approach is verified on Tennessee Eastman (TE) process dataset. Experiment results demonstrate that the EOSSA-LightGBM-based approach is superior to other algorithms.