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51 21 Vol.51 No.21
2015 11 JOURNAL OF MECHANICAL ENGINEERING Nov. 2 0 1 5
DOI 10.3901/JME.2015.21.049
*
( 710049)
TH17
A Deep Learning-based Method for Machinery Health Monitoring
with Big Data
LEI Yaguo JIA Feng ZHOU Xin LIN Jing
(State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049)
Abstract Mechanical equipment in modern industries becomes more automatic, precise and efficient. To fully inspect its health
conditions, condition monitoring systems are used to collect real-time data from the equipment, and massive data are acquired after
the long-time operation, which promotes machinery health monitoring to enter the age of big data. Mechanical big data has the
properties of large-volume, diversity and high-velocity. Effectively mining characteristics from such data and accurately identifying
the machinery health conditions with advanced theories become new issues in machinery health monitoring. To harness the properties
of mechanical big data and the advantages of deep learn
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