Open AccessDevelopment of Mechanical Equipment Fault Diagnosis System Based on Big Data Technology
Author(s) -
Gang Sun,
Yongming Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1648/4/042050
Subject(s) - fault (geology) , big data , engineering , research development , china , systems engineering , computer science , manufacturing engineering , data mining , paleontology , test (biology) , seismology , biology , political science , law , geology
With the development of the times, social progress, the level of human science and technology and the continuous development of modern industry, now, the development and research of mechanical equipment fault diagnosis system in China is facing unprecedented challenges. In today’s big data era, the combination of big data technology and the development and research of mechanical equipment fault diagnosis system in China has become the inevitable demand of the development of times. Therefore, in order to better make the development and research of mechanical equipment fault diagnosis system conform to the development trend of the times, this paper deeply studies the development and research status of mechanical equipment fault diagnosis system in recent years through Internet and big data technology, and analyzes the development and research potential of mechanical equipment fault diagnosis system in recent years, In the research, it is found that the development and research development of mechanical equipment fault diagnosis system in the new era tends to be more intelligent. Therefore, the development and research scheme of mechanical equipment fault diagnosis system suitable for the development requirements of the new era is formulated. It is found that the big data analysis method proposed in this paper plays a key role in the development and research of mechanical equipment fault diagnosis system, and the accuracy of mechanical equipment fault diagnosis reaches 92.5%.