Title :
Application of the Wavelet-SOFM Network in Roll Bearing Defect Diagnosis
Author :
He, Wei ; Zhou, Xiang
Author_Institution :
Wuhan Tech. Coll. of Commun., Wuhan, China
Abstract :
The roll bearing is a main driving device in the modern rotational machine equipments at the present time. In many cases, the accuracy of the instruments and devices used to monitor and control the system is highly dependent on the dynamic performance of the roll bearings. In this paper, A novel method of pattern recognition and fault diagnosis in roll bearing based on the wavelet-neural network is proposed according to the frequency spectrum characteristics of vibration signal. This paper presents an approach for roll bearing fault diagnosis using neural networks and time/frequency-domain bearing vibration analysis. Vibration simulation is used to assist in the design of various roll bearing fault diagnosis. The simulation testing results obtained indicate that neural networks can be effective agents in the diagnosis of various bearing faults through the measurement and interpretation of bearing vibration signal.
Keywords :
fault diagnosis; flaw detection; mechanical engineering computing; pattern recognition; rolling bearings; self-organising feature maps; time-frequency analysis; vibrations; wavelet transforms; fault diagnosis; frequency spectrum characteristics; pattern recognition; roll bearing defect diagnosis; roll bearing driving device; rotational machine equipment; self-organizing feature map neural network; time/frequency-domain bearing vibration analysis; vibration signal; wavelet-SOFM network; Fault diagnosis; Frequency; Machinery; Neural networks; Signal analysis; Signal processing; Signal resolution; Vibration measurement; Wavelet analysis; Wavelet packets; fault diagnosis; roll bearing; self-organizing feature map; vibration signal; wavelet packet;
Conference_Titel :
Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
Conference_Location :
Xiamen
Print_ISBN :
978-0-7695-3571-5
DOI :
10.1109/GCIS.2009.132