Semantic Scholar Open Access 2019 722 sitasi

A survey on Deep Learning based bearing fault diagnosis

Duy-Tang Hoang Hee-Jun Kang

Abstrak

Abstract Nowadays, Deep Learning is the most attractive research trend in the area of Machine Learning. With the ability of learning features from raw data by deep architectures with many layers of non-linear data processing units, Deep Learning has become a promising tool for intelligent bearing fault diagnosis. This survey paper intends to provide a systematic review of Deep Learning based bearing fault diagnosis. The three popular Deep Learning algorithms for bearing fault diagnosis including Autoencoder, Restricted Boltzmann Machine, and Convolutional Neural Network are briefly introduced. And their applications are reviewed through publications and research works on the area of bearing fault diagnosis. Further applications and challenges in this research area are also discussed.

Topik & Kata Kunci

Computer Science

Penulis (2)

Duy-Tang Hoang

Hee-Jun Kang

Format Sitasi

APA MLA BibTeX

Hoang, D., Kang, H. (2019). A survey on Deep Learning based bearing fault diagnosis. https://doi.org/10.1016/j.neucom.2018.06.078

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →

Lihat di Sumber doi.org/10.1016/j.neucom.2018.06.078

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 722×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.neucom.2018.06.078
Akses: Open Access ✓