Application of improved Double-Dictionary K-SVD for compound-fault diagnosis of rolling element bearings

Abstract This paper proposes an improved double-dictionary K-singular value decomposition (IDDK-SVD) algorithm for the compound fault diagnosis of rolling element bearings under complex industrial environments. In the framework, a double-dictionary is first designed for respectively identifying and distinguishing compound-fault features. In addition, an atom selection strategy based on fault information is constructed by combining the Gini index with envelope periodic modulation intensity, which can simultaneously take the periodicity and impulsivity into consideration to find a set of atoms most related to fault for the update of double-dictionary. Finally, an estimation method for calculating the residual error in the sparse coding stage is also presented to meet a better result. Benefitting from these improvements, the proposed IDDK-SVD is effective in the application of compound-fault diagnosis, which is verified by both simulation signals and real signals collected from the locomotive bearing test rig.