A review on empirical mode decomposition in fault diagnosis of rotating machinery

Applications of machine learning to machine fault diagnosis: A review and roadmap

Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis. Two main contributions are concluded by comparing to the previous works: first, the proposed model can utilize domain adaptation meanwhile strengthening the representative information of the original data, so that a high classification accuracy in the target domain can be achieved, and second, we proposed several strategies to explore the optimal hyperparameters of the model. Experimental results, on several real-world datasets, demonstrate the effectiveness and the reliability of both the proposed model and the exploring strategies for the parameters.

YuDejie

Papers

A roller bearing fault diagnosis method based on EMD energy entropy and ANN