Deep Learning Based Approach for Bearing Fault Diagnosis

By Miao He, David He. IEEE transactions on industry applications, 2017

421 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/255a01f9af7edaab2134843a89bae5cb9f000031

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. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler

In Mechanical Engineering

1. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler

Citations in the corpus

• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, background): “For example, in [161], a new large memory storage retrieval (LAMSTAR) neural network is proposed with 1 input layer, 40 input SOM modules as hidden layers, and 1 decision SOM module as the output layer.”
• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “Specifically, the use of acoustic sensors should be advocates since it is reported in [173] that in comparison with vibration signals, acoustic emission signals “have certain advantages in detecting incipient faults, capturing and representing”.”