Denoising convolutional autoencoder configuration for condition monitoring of rotating machines

Abstract

In the present state of technology, rotating machine health monitoring is becoming economically mandatory. There is presently a clear trend to apply deep learning techniques to this area. This work analyzes some of the main supervised and unsupervised neural network current architectures and, as a consequence, proposes a novel denoising convolutional autoencoder model configuration applied to machine health monitoring. The configuration aims the main advantages of different conventional architectures for performance optimization over normalized time series signals. Experiments carried out based on vibration data extracted from bearings helped to validate the developed model and to determine the structure that offer the most satisfactory results. A comparison is made in terms of accuracy and robustness to noise between the proposed method and some related methods, including other published models. The objective is to assess the effectiveness of the proposed method through using two public experimental datasets produced under different machine operating conditions. The experimental results show that the proposed architecture provides greater classification accuracy and robustness in both case studies considering other published models.