Feature selection tree for automated machinery fault diagnosis
Intelligent machinery fault diagnosis commonly utilises statistical features of sensor signals as the inputs for its machine learning algorithm. Due to the abundance of statistical features that can be extracted from raw signals and the accuracy of inserting all the available features into the machi...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2019-01-01
|
Series: | MATEC Web of Conferences |
Online Access: | https://doi.org/10.1051/matecconf/201925502004 |