An enhancement deep feature fusion method for rotating machinery fault diagnosis

By Haidong Shao, Hongkai Jiang, Fuan Wang, Huiwei Zhao. 2017

293 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Dong Yang
2. Weiting Zhang
3. Hongchao Wang
4. Haidong Shao
5. Hongkai Jiang
6. Ala Al-Fuqaha
7. Mohsen Guizani
8. Sameh Sorour

In Mechanics of Materials

1. Tianchen Liang
2. Haizhou Zhang
3. Haidong Shao
4. Hongkai Jiang

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “[146] used DNNs for feature extraction in a fault diagnosis (also referred as fault detection and classification (FDC)) system for rotating devices.”
• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “Moreover, an enhancement feature fusion method [108] based on deep AE was developed for rotating machinery and was constructed with a denoising AE and a contractive AE for improving the feature learning ability.”
• Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network (2018, background): “DAE is easy to train and it is a purely unsupervised feature learning model [21].”
• A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method (2018, unknown): “ing auto-encoder [14], [15], and sparse filtering [16].”