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Author:

Gao, Li-Xin (Gao, Li-Xin.) | Zhai, Fen-Lou (Zhai, Fen-Lou.) | Yang, Zi-Jing (Yang, Zi-Jing.) | Su, Shan-Bin (Su, Shan-Bin.)

Indexed by:

EI Scopus

Abstract:

In modern industry especially the process industry, can extensively exist a large amount of low-speed and heavy-duty equipments such as industrial excavator, port unloader machine, portal jib crane, increasing gearbox of wind power generation and so on. Once a fault occurs on these key machines, longer maintenance time and higher cost will be needed while the normal production order of enterprises will also be disorganized which can cause huge losses to enterprises. Outstanding characteristics of such equipments are heavy burden quantity and low-speed, some of which can also be accompanied with intermittent operation or great impact load. Hence, potential failure is quite difficult to be detected by traditional monitoring and diagnosis technique due to all the above characteristics. Based on strong noise and weak signals of potential failure produced during the operation of low-speed and heavy-duty equipments, parameters such as oil, vibration and current are proposed to make the detection and diagnosis through which preliminary results have been obtained.

Keyword:

Outages Speed Wind power Electric power generation Condition monitoring

Author Community:

  • [ 1 ] [Gao, Li-Xin]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Zhai, Fen-Lou]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Yang, Zi-Jing]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 4 ] [Su, Shan-Bin]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, China

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Source :

Year: 2010

Page: 191-197

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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