Open AccessTypical status recognition of gearbox based on big data
Author(s) -
Lixia Zhang,
Zhou Yang,
Hongyu Wang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/7/072066
Subject(s) - python (programming language) , big data , computer science , time domain , frequency domain , fault (geology) , domain (mathematical analysis) , data mining , pattern recognition (psychology) , artificial intelligence , computer vision , seismology , mathematics , geology , mathematical analysis , operating system
In this paper, the experimental data of five different working states of the gearbox during the normal operation and the failure of the gearbox are taken as the research object, the python-based data analysis tools Numpy and Pandas are used to establish a fault detection model, by collecting time-domain and frequency-domain data of several faults such as fracture, crack and wear of transmission gear, and analyzing the characteristic parameters of the data, the classification of transmission faults is realized, thus laying a foundation for the application of big data tools in fault diagnosis and analysis.