Sparse classification of rotating machinery faults based on compressive sensing strategy

Rotating machinery is integral to production processes and is composed of several components susceptible to malfunction. Safety and stable operation necessitate early identification of potential faults. Fault identification and classifications for rotating machinery are investigated in this study utilizing expanded monitoring data. A representation classification strategy for rotating machinery faults is developed based on a newly developed compressive sensing theory focusing on extraction and classification of fault features through sparse representation combined with random dimensionality reduction mapping. Original characteristics of a vibration signal are sampled and preserved by applying a small number of random projections and a learning redundant dictionary then constructed to sparsely represent the vibration signal. Fault signal impulse information is determined through an optimization strategy with sparsity promoting. A rolling bearing is utilized as an example in the study with simulations and experiments indicating that fault characteristics may be directly extracted from a small number of random projections without ever reconstructing the vibration signal completely and with minimal prior fault characteristic frequency knowledge required. (C) 2015 Elsevier Ltd. All rights reserved.