Rolling bearing sound signal fault feature extraction method based on STFT and rotation inertia entropy

The invention discloses a rolling bearing sound signal fault feature extraction method based on an STFT and a rotation inertia entropy. A common rolling bearing fault feature extraction method is based on a rolling bearing vibration signal, however the requirement of a sensor by the collection of rolling bearing vibration data is very high, the equipment cost is increased, an intelligent mobile phone is taken as an important part of daily life, and the recording function of the mobile phone can collect a rolling bearing sound signal. The invention provides the rolling bearing sound signal fault feature extraction method based on short time Fourier transform (STFT) and a rotation inertia entropy, firstly the intelligent mobile phone is used to collect a rolling bearing fault sound signal, then the sound signal is subjected to Fourier analysis, a spectrogram matrix is obtained, then the modulus of the matrix is obtained, and the rotation inertia entropy of the spectrogram of the rotation inertia entropy is calculated. A test result analysis shows that the fault feature obtained by the method has an excellent classification characteristic and can support the fault diagnosis of a rolling bearing.