Fault diagnosis of belt conveyors using audio data based on LLE with adaptive neighborhood and neighbor optimization under multi-information fusion

Abstract

To solve the problem of insufficient dimensionality reduction performance of dimensionality reduction algorithms in fault diagnosis of belt conveyors using audio data (FDBCA), we propose a locally linear embedding (LLE) algorithm with adaptive neighborhood and neighbor optimization under multi-information fusion (FM-ANO-LLE) for FDBCA. This method first uses a multi-information fusion metric to assess the relationships between samples from multiple perspectives and improve the accuracy of the selected samples. Second, an adaptive neighborhood strategy based on sample metric discriminatory information is used to dynamically divide the local structure of the sample. Finally, the reconstruction weights are optimized through the nearest neighbor optimization strategy to improve the dimensionality reduction performance. The experimental results demonstrated that FM-ANO-LLE outperforms existing dimensionality reduction algorithms, the performance of the dimensionality reduction algorithm is significantly improved, and it achieves higher diagnostic accuracy in practical FDBCA, with an accuracy of 90.4%.