White noise

Gene

Quantum mechanics

Chebyshev filter

Algorithm

Normalization (sociology)

Hilbert–Huang transform

Statistics

Robustness (evolution)

Polar coordinate system

Eigenvalues and eigenvectors

Biochemistry

Computer science

Fault (geology)

Anthropology

Seismology

Geology

Physics

Singular value decomposition

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Chebyshev polynomials

Sociology

Mathematical analysis

Artificial intelligence

Geometry

Chemistry

Noise reduction

Bearing (navigation)

Singular value

Computer vision

Measurement

A novel bearing fault diagnosis on basis of empirical mode decomposition (EMD) and improved Chebyshev distance is presented. After normalization, each group of sample data is divided into 10 equal parts on average. EMD is used to decompose the equal part signal into several eigenmode functions, and the first five intrinsic mode function (IMF) components are retained and transformed into symmetrical in polar coordinates by symmetrized dot pattern (SDP) method, each SDP image is processed via binarization and localization, then the local SDP images are averaged to obtained the mean image as benchmark. The maximum eigenvalue of the average matrix after denoising is computed, in this way the improved Chebyshev distance is constructed and bridges the gap between the local matrix of each IMF component and the average matrix. Using the improved Chebyshev distance of IMF1 as feature, this method can effectively diagnose the faults of rolling bearing. Finally, test experiments are carried out to verify the accuracy and robustness of present approach.

Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image