Intelligent fault diagnosis method of mine hoist based on knowledge engineering

被引：0
|
作者：
Li J.-L. [1 ,2 ]
Yang Z.-J. [1 ]
Pang X.-Y. [1 ]
机构：
[1] College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan
[2] Post-doctoral Scientific Research Station, Shanxi Coking Coal Group Co., Ltd., Taiyuan
来源：
| 1600年 / China Coal Society卷 / 41期
关键词：
Fault diagnosis; Knowledge acquisition; Knowledge reasoning; Knowledge representation; Mine hoist;
D O I：
10.13225/j.cnki.jccs.2015.1234
中图分类号：
学科分类号：
摘要：
To overcome the instability of the diagnostic reasoning results caused by the difficulty in knowledge acquisition, the single knowledge representation, and the poor self-adaptation ability of fault diagnosis reasoning method in traditional hoist fault diagnosis systems, the hoist fault diagnosis method based on knowledge engineering is investigated. Fault diagnostic rule knowledge acquisition methods based on improved attribute importance is proposed, and it provides a data basis for hoist fault diagnosis. The mine hoist fault diagnostic ontology knowledge base is constructed and the fault diagnostic ontology knowledge representation methods based on OWL DL and fault diagnostic rule knowledge representation methods based on SWRL are proposed, and the hoist system structure and the diagnosis knowledge integration are implemented. The probability of the ontology knowledge is extended, and a new fault diagnosis uncertainty knowledge reasoning method is proposed, which are based on ontology and Bayesian. Based on the theory and method above, the fault monitoring and diagnosis system of the mine hoist is developed, and the method is proved to be feasible and reliabile. © 2016, Editorial Office of Journal of China Coal Society. All right reserved.
引用
收藏
页码：1309 / 1315
页数：6
相关论文
共 17 条
  • [1] Ji Z., Huang J., Qin S., Gearbox fault diagnosis using lifting wavelet, Journal of Vibration, Measurement & Diagnosis, 30, 3, pp. 291-294, (2010)
  • [2] Kiran V., Hemantha K., Gangadharan K.V., Fault diagnosis of deep groove ball bearing through discrete wavelet features using support vector machine, International Journal of Comadem, 17, 3, pp. 31-37, (2014)
  • [3] Zhang C., Yuan Y., Application of frequency modulated EMD in bearing fault diagnosis, Journal of Vibration and Shock, 33, 18, pp. 185-189, (2014)
  • [4] Li X., Li P., Jiang L., Class mean kernel principal component analysis and its application in fault diagnosis, Journal of Mechanical Engineering, 50, 3, pp. 123-129, (2014)
  • [5] Lei Y., Yang Z., Application of neural network for fault diagnosis of hoist, Journal of South China University of Technology, 38, 2, pp. 67-72, (2010)
  • [6] Elhadef, Mourad, Romdhane, Et al., Fault diagnosis using partial syndromes: A modified Hopfield neural network approach, International Journal of Parallel, Emergent and Distributed Systems, 29, 2, pp. 119-146, (2014)
  • [7] Liu X., Xu G., Ren S., Et al., Hoist machinery fault monitoring based on HHT-Ddkica and support vector data description method, Journal of Mechanical Engineering, 48, 23, pp. 65-75, (2012)
  • [8] Wang J., Development and prospect on fully mechanized mining in Chinese coal mines, International Journal of Coal Science & Technology, 1, 3, pp. 153-260, (2014)
  • [9] Wang J., Yu B., Kang H., Et al., Key technologies and equipment for a fully mechanized top-coal caving operation with a large mining height at ultra-thick coal seams, International Journal of Coal Science & Technology, 2, 2, pp. 97-161, (2015)
  • [10] Du S., A new method for fault diagnosis of mine hoist based on manifold learning and genetic algorithm optimized support vector machine, Elektronika Ir Elektrotechnika, 123, 7, pp. 99-102, (2012)
12