Multi Scale Shift Invariant Sparse Coding for Robust Machinery Diagnosis

An efficient shift invariant sparse coding (SISC) algorithm which combined multi scale information was proposed for machinery fault diagnosis. The SISC was applied as a classifier for fault diagnosis, and it could directly train and recognize the vibration signals without feature extraction or pre-processing. What's more, multi scale SISC classifiers were combined for better performance. Validated by experiments, although the loads of training samples and testing samples were not the same, this scheme could still precisely determine the fault location as well as the severity of fault for bearings. Compared with other methods, the proposed algorithm shows high accuracy, strong robustness and a certain value for engineering application.