Research on the enhancement of machine fault evaluation model based on data-driven

Abstract

Recently fault data diagnosis-based deep learning methods have achieved promising results. However, most of these methods' performances are difficult to improve once they have achieved accuracy. This paper mainly uses fusion theory based on data-driven to solve this problem. Firstly, the diagnostic models are divided into feature extraction and neural network. Then, four feature extraction methods are fused by pre-allocation. The neural network part consists of three single models, and the weight of the three output results is determined by regression analysis. Experiments show that the accuracy of diagnostic models is improved. Finally, we combine the two studies and propose a Fusion-Ensemble superposition (FES) model. The AUC value of the model is higher than 98% in most tasks of the DCASE2020 machine failure dataset.