An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings

By Pramod Bangalore, Lina Bertling Tjernberg. IEEE Transactions on Smart Grid, 2015

327 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/50afeb8b9aabe2756fc013f326b96ac67b8616c3

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. René de Jesús Romero-Troncoso
2. Sheraz Ali Khan
3. Jong-Myon Kim
4. Roque Alfredo Osornio-Rios

In Safety, Risk, Reliability and Quality

1. Sheraz Ali Khan
2. Jong-Myon Kim

Citations in the corpus

• Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain (2016, background): “In gearboxes, however, the possibility of gear fault appearing is lower, around 26%, but an early stage fault, as uniform wear, may cause severe consequences [9, 10].”
• Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions (2016, methodology): “These techniques usually involve threemajor steps: (1) measurement of the signal that will be used for fault diagnosis (different types of signals, such as structural vibration [10, 15–18], stator current for bearings in induction motors [7, 8], acoustic emissions [19–25], temperature [6], and more ”