A Fault Diagnosis Method for Business Management System Based on Convolutional Neural Network

Meini Li^1*Zihao Wang²

• ¹Sichuan Agricultural University School of Business and Tourism, ChengDu, China
• ²Google Inc, California, United States

The complexity and dynamism of business management system pose higher requirements for the accuracy and timeliness of fault diagnosis. This paper proposes a compensation distance evaluation technique-kernel principal component analysis-convolutional neural network-bidirectional long short-term memory network (CDETKPCA-CNN-BiLSTM) that integrates attention mechanism to address the limitations of traditional diagnostic methods in nonlinear and high-dimensional data scenarios. The bidirectional long short-term memory (BiLSTM) layer and attention mechanism layer further improve the accuracy and reliability of fault diagnosis. Feature extraction is performed in business management system data from both time and frequency domains, effectively utilizing temporal information to form an initial feature set. To address the issue of data redundancy in business management system, a compensation distance evaluation technique and kernel principal component analysis (CDETKPCA) feature fusion method is proposed. Through CDET, the initial feature set is screened and weighted to guide KPCA feature fusion processing, generating a fused feature set for subsequent fault diagnosis research. The experimental results show that CDETKPCA-CNN-BiLSTM can extract effective information more efficiently and significantly improve analysis accuracy. And this provides a new technical method for fault diagnosis in business management system.