Given that the prior knowledge of all kinds of bearing friction degradation model cannot be attained usually

starting from the similarities of different states

a bearing friction faults state recognition algorithm without prior knowledge was proposed based on sparse representation and absolute grey relational degree of B-mode (AGRDB). First

for the defects of sparse representation without supervision

the AGRDB was involved in the sparse representation

to get normal and severe friction codes under the largest distance between classes and smallest distance within the classes. Second

sparse representation model with discriminant sex was established under the same dictionary. And current state of the bearing was identified by comparing sparse coding and reconstruction error of normal lubrication and serious friction. Finally

the results of simulation signal and diesel engine bearing experiment show that the proposed method can better identify the sliding bearing early friction state (100-216min) and serious friction state (216-384min) under the less prior knowledge. And this algorithm is suitable for plain bearing fault monitoring under less samples.