Abstract:  Aiming at the problem of poor nonlinear approximation performance of existing mechanical equipment electronic fault signal detection methods, a detection algorithm based on optimal sparse coding learning was proposed. The sparse expression was used to identify the electronic fault signal of mechanical equipment, which improved the global optimization ability of the detection algorithm and avoided getting into the local optimal solution. The sparse solution with higher accuracy was obtained by improving the matching degree between the atomic structure and fault signal in the over-complete dictionary model. The periodicity of the reconstructed signal after sparse approximation was consistent with the original signal, the feature self-learning scheme was introduced. Finally the sparse representation of each segment of the signal was extracted in a segmented way, and the control and detection performance of the original fault signal was improved. The results show that the proposed detection algorithm has a higher matching degree with the original signal periodic peak in signal fault feature extraction, a lower control error in reconstructed signal, and a better time consumption when the sparsity value exceeds 100 than the existing methods.

Key words:  optimized sparse coding; relaxation algorithm; sparse solution; self-learning; mechanical equipment electronic fault detection