Intelligence Monitor and Diagnosis to High Speed Brushes Aeration Mechanics Based on Turbulent Flow Displacement Sensing Theory

Abstract:

High Speed Brushes Aeration Mechanics are the effective aeration equipments which are widely used in the environmental protection. Because of the big span of main spindle and its high speed when it is working, the breakdown sometimes occurs. It is very importance to monitor its condition and diagnose its breakdowns. Turbulent Flow Displacement Sensors are the non-contact types which are based on eddy current effect. It has many advantages, such as good linearity, wide frequency response scope, convenience installment and so on. So it is very suitable for the main spindle’s vibration signals of a high speed brushes aeration mechanic are monitored. With the development of Artificial Neural Networks technology, the equipment breakdown diagnosis has realized intellectualization. The recognition of equipment failure types is one of the most important studying domains of Artificial Neural Networks at present. Based on the research of eddy current effect and Artificial Neural Networks, we build up a test system which can monitor condition and diagnose breakdown to a GSB-12 high speed brushes aeration mechanic. With the help of it, the vibration signals of the measurement points on the main spindle are measured at two mutually vertical positions. The signals’ base frequency and multiplicative frequency are taken as characteristic value. Six common breakdowns are selected and to be taken as the standard sample and there are 3 lays in the neural network. Using FBP algorithm, we get a satisfied effect. The experiment has confirmed that this method is advanced, reliable and practical. It provides a new method about intelligent monitor and breakdown diagnosis to high speed brushes aeration mechanics’ condition.

• Periodical: Key Engineering Materials (Volumes 426-427)
• Pages: 191-196
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.426-427.191
• Online since: January 2010
• Authors: H.I. Liu, X.P. Li, Yan Nian Rui, Ying Ping He
• Keywords: Artificial Neural Network (ANN), Breakdown Diagnosis, Eddy Current Test, Intelligent Monitor

References

[1] D.Q. Zhu, H. Shi: Artificial eural etworks Principle and Application (Technical Publishing House, Beijing 2006).

[2] Y.X. Xu, P.X. Yuan and P.W. Yang: Complex Mechanism Breakdown Diagnosis Fractal and Wavelet Method (Mechanical Industry Publishing House, Beijing 2003).

[3] Y.N. Rui: Based on Piezoelectricity Impedance Equipment Structure Healthy Intelligence Diagnosis, edtied by Chinese Manufacturing Industry Informationization, Nanjing (2003).

[4] V. Giurgiutiu and C.A. Rogers.: Proc. SPIE Nondestructive Evaluation Techniques for Aging Infrastructure & Manufacturing, Scottsdale, AZ2946 (2006) pp.211-218 Frequency band Displace ment.