The invention discloses a bearing fault early warning and diagnosis method based on improved MSET and
frequency spectrum characteristics. The method comprises the following steps: obtaining an original vibration
signal; obtaining a fault characteristic frequency, and determining an actual frequency range according to the variable quantity of the fault characteristic frequency; carrying out envelope
spectrum analysis: filtering the original vibration
signal by using a fast spectrum kurtosis and band-pass filtering method, analyzing the filtered vibration
signal to obtain an envelope spectrum, and further obtaining monitoring parameters; carrying out improved MSET modeling: establishing a historical memory matrix, establishing an MSET model by using the obtained historical memory matrix, and calculating an
estimation vector of a vibration signal obtained in real time and a residual error of each monitoring parameter; carrying out fault early warning: constructing a
similarity model by using the overall deviation degree and the residual deviation degree, calculating a similarity value of a historical memory matrix, constructing a monitoring threshold value, and performing fault early warning
decision making; and carrying out fault diagnosis: constructing a fault contribution rate model by using the residual contribution degree and the frequency amplitude contribution degree for the signal after the early warning is sent out, and diagnosing the fault part of the bearing.