For an established fault diagnosis system which is based on it own expert system, it is usually incapable to diagnosis the new operating conditions, of which the knowledge has not been explored by the system. It is the purpose of the paper to develop the approach of identifying new fault and self-learning for diagnosis based on non-linear fractal theorem. It has been generally accepted that the vibration series has obvious fractal feature, which can reflect the essential characteristics of new fault. When the novel fault is taken place in the system, a related sub-net is increased to the system and trained with this sample. We have verified experimentally that the fractal dimensions of the same class faults are distributed approximately around a definite value that can represents the dimension of the standard sample for the novel fault. Based on non-linear theorem, the approach of identifying new fault and self-learning for diagnosing is put forward.
Skip Nav Destination
ASME 2005 Power Conference
April 5–7, 2005
Chicago, Illinois, USA
Conference Sponsors:
- Power Division
ISBN:
0-7918-4182-0
PROCEEDINGS PAPER
Study on Self-Learning for Vibration Fault Diagnosis System of Rotating Machinery
Zhihua Ge,
Zhihua Ge
North China Electric Power University, Beijing, P. R. China
Search for other works by this author on:
Yuguang Niu,
Yuguang Niu
North China Electric Power University, Beijing, P. R. China
Search for other works by this author on:
Zhiping Song,
Zhiping Song
North China Electric Power University, Beijing, P. R. China
Search for other works by this author on:
Zhongguang Fu
Zhongguang Fu
North China Electric Power University, Beijing, P. R. China
Search for other works by this author on:
Zhihua Ge
North China Electric Power University, Beijing, P. R. China
Yuguang Niu
North China Electric Power University, Beijing, P. R. China
Zhiping Song
North China Electric Power University, Beijing, P. R. China
Zhongguang Fu
North China Electric Power University, Beijing, P. R. China
Paper No:
PWR2005-50123, pp. 347-352; 6 pages
Published Online:
October 27, 2008
Citation
Ge, Z, Niu, Y, Song, Z, & Fu, Z. "Study on Self-Learning for Vibration Fault Diagnosis System of Rotating Machinery." Proceedings of the ASME 2005 Power Conference. ASME 2005 Power Conference. Chicago, Illinois, USA. April 5–7, 2005. pp. 347-352. ASME. https://doi.org/10.1115/PWR2005-50123
Download citation file:
7
Views
Related Proceedings Papers
Related Articles
Vibration Response-Based Intelligent Non-Contact Fault Diagnosis of Bearings
ASME J Nondestructive Evaluation (May,2021)
Pattern Recognition for Automatic Machinery Fault Diagnosis
J. Vib. Acoust (April,2004)
Looseness Diagnosis of Rotating Machinery Via Vibration Analysis Through Hilbert–Huang Transform Approach
J. Vib. Acoust (June,2010)
Related Chapters
Vibration Monitoring for Fault Diagnosis in Rotating Machinery Using Wavelet Transform
International Conference on Advanced Computer Theory and Engineering, 4th (ICACTE 2011)
Application of Improved Wavelet Neural Network to Fault Diagnosis of Pumping Wells
International Conference on Mechanical Engineering and Technology (ICMET-London 2011)
High-Speed Automatic Mechanism Fault Diagnosis Based on Motion Modality and Information Entropy
International Conference on Computer and Computer Intelligence (ICCCI 2011)