Compressed sparse time-frequency feature representation via compressive sensing and its applications in fault diagnosis

Feature extraction in time-frequency domain is wildly used in fault diagnosis of rotating machines. However, it needs more time and space to store the time-frequency information, which restricts its practical applications, especially for remote health monitoring. A novel parallel FISTA-like proximal decomposition algorithm was proposed for reconstruction of sparse time-frequency representation (TFR) from the limited noisy observations based on the recently developed compressive sensing. The effectiveness of recovering buried sparse signatures was demonstrated by numerical simulations. The proposed method yielded better results than those obtained by the traditional RecPF method. A novel framework for remote machine health condition monitoring was then developed via the proposed algorithm and the advancements in wireless communication. The effectiveness of the new proposed method for the sparse TFR in detecting bearings and gears defects in rotating machines is further verified using many practical cases. These results illustrate the proposed method can well retain TF signatures without clearly artifacts in the recovered TFR using only very limited measurements.