A python']python based tutorial on prognostics and health management using vibration signal: signal processing, feature extraction and feature selection

被引：2

作者：

Sim, Jinwoo ^{[1
]}

Min, Jinhong ^{[1
]}

Kim, Doyeon ^{[1
]}

Cho, Seong Hee ^{[1
]}

Kim, Seokgoo ^{[2
]}

Choi, Joo-Ho ^{[3
]}

机构：

[1] Korea Aerosp Univ, Dept Aerosp & Mech Engn, Goyang Si, Gyeonggi Do, South Korea

[2] Univ Florida, Dept Mech & Aerosp Engn, Gainesville, FL USA

[3] Korea Aerosp Univ, Sch Aerosp & Mech Engn, Goyang Si, Gyeonggi Do, South Korea

来源：

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY | 2022年 / 36卷 / 08期

基金：

新加坡国家研究基金会;

关键词：

Prognostics and health management (PHM); !text type='Python']Python[!/text; MATLAB; Tutorial; Bearing; Gear; Signal processing; Feature engineering; EMPIRICAL MODE DECOMPOSITION; FAULT-DIAGNOSIS; HILBERT SPECTRUM; EMD METHOD; ENTROPY;

D O I：

10.1007/s12206-022-0728-z

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

MATLAB is a convenient and well-established engineering tool used by many researchers and engineers in implementing the prognostics and health management (PHM). Recently however, Python has emerged as a new language platform for the same purpose due to its advantages of free access, high extensibility and plenty libraries. This paper provides a Python tutorial to aid the beginners in the PHM to implement the signal processing and feature engineering using the open access data of gears and bearings. The Python codes are provided at the web page so that they produce the same results as the MATLAB codes. As such, they are reliable as well as of practical value to those who want to learn how to implement the PHM by Python or to migrate from the MATLAB to Python.

引用

页码：4083 / 4097

页数：15

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