Collaborative Sparse Low Rank Regularization for Aero-Engine Bearing Fault Diagnosis

It is very difficult to detect weak signatures for the early fault diagnosis of aero-engine bearings due to fewer vibration transducers and stronger non-Gaussian noises. Therefore, prior knowledges of latent subcomponents of aero-engine vibration signals are investigated, then the low rank pattern of the fault features in the tailored two-dimensional transformation space and the sparse structure of harmonic interference signals in the frequency domain are revealed. Moreover, the low-rank regularization in spatial domain and the sparse regularization in spectral domain are established. Based on the two regularization priors, a collaborative sparse low-rank model (CSLM) is proposed. The CSLM emphatically exploits structural differences of fault signals and interference signals in different transformation spaces, and further describes these differences in two completely uncoupled spaces, which provides a favorable way to construct highly incoherent dictionaries for sparse decomposition strategy. The simulation results verify that the proposed method can decouple the hidden impulsive features from strong harmonic interferences and noises, and meanwhile reliably discover fault sources. Two aero-engine bearing experiments indicate that the CSLM can effectively detect the fault patterns of aero-engine bearing with spalling area of 1.0 mm2 at running speed up to 1 8000 r/min, and reliably identify the weak characteristic information of bearing faults even in the initial stage of an accelerated fatigue life cycle experiment. © 2021, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.