Application of group decomposition and 1.5-dimensional spectra to acoustic composite fault diagnosis of rolling bearings

Abstract Under the background of strong noise and mutual interference coupling of each fault, the acoustic compound fault diagnosis of rolling bearing is very challenging。 For the composite fault features which are difficult to be extracted due to strong noise interference and uneven distribution of fault intensity, put forward the optimization of swarm decomposition combined with 1.5-dimensional(1.5-d) spectrum method of acoustic composite rolling bearing fault feature separation. The method firstly uses the composite index to iteratively search for the optimal group decomposition threshold value, the adaptive group decomposition of composite fault acoustic signals is realized by optimal parameter group disassembly, and then selects the sensitive components for the decomposed components, and then further analyzes the envelope signal of the sensitive components to reduce the redundancy components and the noise interference, and selects the 1.5-d spectrum to further analyze the envelope signal, thus realizes the effective separation of the composite faults of the rolling bearings acoustic faults. Rolling bearing simulations and experimental acoustic signals verify the validity of the proposed method, and this work gives a new tool for composite fault diagnosis of revolving machinery.