Abstract
ABSTRACT When diagnosing the weak fault of rolling bearing, the fault characteristic is difficult to be extracted because the fault signal has a small amplitude and is susceptible to noise. Aiming at this problem, a fault diagnosis method is proposed based on fractional Fourier transform (FRFT) and deep belief networks (DBN). The original fault signal is first transformed into the fractional domain, and the signal is filtered in this domain to extract the fault features. The characteristic signal is then input to the DBN, and the whole network is optimized to finally realized fault diagnosis by using the pre-training and the reverse propagation algorithm. The simulation results show that the method can effectively detect the weak fault of rolling bearing.
Highlights
Rolling bearing has been widely used in rotating machinery due to its high bearing capacity and low friction coefficient, and it is one of the most vulnerable parts of rotating machinery (Zhong & Huang, 2007)
The diagnosis accuracy rate of the general fault of the outer ring is lower because the frequency characteristics of it is more obvious, and the time-domain waveform of the weak fault is similar to the general fault waveform after the fractional Fourier transform (FRFT) process
Before the FRFT processing, the diagnostic accuracy rate for weak faults is only 50%, but after FRFT filtering, the accuracy rate of weak fault diagnosis is increased to 90%
Summary
Rolling bearing has been widely used in rotating machinery due to its high bearing capacity and low friction coefficient, and it is one of the most vulnerable parts of rotating machinery (Zhong & Huang, 2007). Since the weak fault of the rolling bearing has the characteristics of small amplitude, large noise, nonlinearity and instability, the traditional time–frequency analysis method is not applicable. The proposed method inputs the fault signal obtained by FRFT into the DBN, and utilizes the learning and classification capabilities of the DBN to optimize the entire network through error feedback, improves the fault classification result, and realize the fault diagnosis of the rolling bearing. The effectiveness of this method is verified by experimental verification and comparison.
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