SIGNAL (programming language)

Interference (communication)

Algorithm

Engineering

Frequency domain

Bandwidth (computing)

Statistics

Kurtosis

Telecommunications

Frequency band

Computer science

Acoustics

Fault (geology)

Seismology

Geology

Physics

Mathematics

Image (mathematics)

Electronic engineering

Artificial intelligence

Time domain

Programming language

Channel (broadcasting)

Bearing (navigation)

Noise (video)

Stochastic resonance

Computer vision

Measurement

The vibration signal collected in the industrial field usually has a low signal-to-noise ratio, which is not enough for the recognition of faults. Aiming at the difficulty of bearing fault diagnosis under strong noise interference, a bearing fault diagnosis algorithm based on spectrum characteristics and MOMEDA is extracted in this paper. First, Hankel matrix is used to split the original time-domain signal to construct a time-domain segmentation matrix to obtain various manifestations of noise in the signal. Then, the signal spectrum is reconstructed by adopting the method of spectrum fusion, effectively eliminating the random characteristics in the noise. Finally, the MOMEDA is used to construct a multi-point kurtosis spectrum and extract the characteristic frequency of the fault with periodic impact characteristics in the side band. Experimental results show that the method proposed in this paper can extract the characteristic frequency of faulty bearing under stronger noise interference.

Research on bearing fault diagnosis based on spectrum characteristics under strong noise interference