Variable Condition Bearing Fault Diagnosis Based on Time-Domain and Artificial Intelligence

Abstract:

Aim at some problem in fault diagnose: the characteristic frequency depends on the speed, the spectrum is complex , which are easy to diagnose error when in the variable conditions, and it is often difficult to identify the fault positioning in the frequency domain. the paper puts forward a new method: Variable condition bearing fault diagnosis basing on time-domain and artificial intelligence , not depend on speed and frequency domain. This method use vibration signal, calculates the kurtosis, skewness, rms etc 12 time-domain value, then these character vectors are sent to the neural network classifier to complete fault type pattern recognition, Finally, the same faults are sent to the next neural network for fault positioning and damage extent identification. The experimental result showed that using this method can obtain very good effect.

• Periodical: Applied Mechanics and Materials (Volume 203)
• Pages: 329-333
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.203.329
• Online since: October 2012
• Authors: Qing Zhong Hu, Shu Lei Zhang, Sheng Yang
• Keywords: Fault Diagnosis, Neural Network (NN), Time-Domain, Variable Condition

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