The invention discloses a
machine tool spindle
bearing fault detection method based on adaptive characteristic mode
decomposition, which comprises the following steps: S1, acquiring an original vibration
signal of a
machine tool spindle bearing, and extracting main frequency components of the
signal; s2, adopting an artificial
hummingbird algorithm for main frequency components of the extracted signals, and optimizing modulation parameters of FMD; s3, performing mode
decomposition on the vibration
signal, and introducing a
badger optimization
algorithm to screen
modal components of FMD; s4, calculating a sparsity influence measurement index for each sub-signal obtained by
decomposition, and screening the sub-signals based on an SIMI index; s5, performing envelope
spectrum analysis on the screened sub-signals, and extracting fault feature frequencies of the sub-signals; and S6, matching and comparing the extracted fault characteristic frequency with a standard fault
database, and determining the fault type of the
machine tool spindle bearing. According to the method,
vibration signal processing, mode decomposition and optimization and fault
feature extraction and analysis technologies are combined, and
machine tool spindle
bearing fault detection based on self-adaptive feature mode decomposition is achieved.