Feature Identification with Compressive Measurements for Machine Fault Diagnosis

machine fault diagnosis collects massive amounts of vibration data about complex mechanical systems. Analyses of the information contained in these data sets have already led to a major challenge. Compressed sensing (CS) theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory. This theory enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. However, it is suboptimal to recover full signal from the compressive measurements and then solve feature identification problems through traditional DSP techniques. Thus, a novel mechanical feature identification method is proposed in this paper. Its main advantage is that fault features are extracted directly in the compressive measurement domain without sacrificing accuracy. Meanwhile, a significant reduction in the dimensionality of the measurement data is achieved and the computational efficiency is improved dramatically. Numerical simulations and experiment are performed to prove the reliability and effectiveness of the proposed method.