论文信息 - On-line fault diagnosing techniques for a class of manufacturing equipment

On-line fault diagnosing techniques for a class of manufacturing equipment

This research develops an intelligent on-line monitoring and diagnosis system for a class of key manufacturing equipment. Important issues concerning the fault diagnosis system are discussed in this paper, including wide-band sensor, high-speed data acquisition for on-line monitoring, advanced signal processing techniques to suppress severe interference in manufacturing environment, and computational intelligence techniques to recognize fault patterns. Successful implementation of the intelligent monitoring and diagnosis system has provided decision-making support for equipment serviceability and condition-based maintenance.

Jing-Bing Zhang | Yi Zhi Zhao | Hui Ma | Guangning Wu | Chak-Huah Tan

[1] I. Blokhintsev,et al. Field experiences with the measurement of partial discharges on rotating equipment , 1999 .

[2] Li Lin,et al. Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets , 2003, J. Intell. Manuf..

[3] Sun-Yuan Kung,et al. Digital neural networks , 1993, Prentice Hall Information and System Sciences Series.

[4] R. Candela,et al. PD recognition by means of statistical and fractal parameters and a neural network , 2000 .

[5] J. P. Zondervan,et al. Fundamental aspects of PD patterns of on-line measurements on turbogenerators , 2000 .

[6] A. Gandelli,et al. A fast hybrid system for PD measurement , 2000 .