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A novel adaptive signal decomposition algorithm, broadband mode decomposition (BMD), is proposed for analyzing nonstationary broadband signals. Unavoidable error will occur when applying former time-frequency methods to broadband signals, which is caused by Gibbs phenomenon and the calculation of extrema. To overcome that problem, BMD is proposed by searching in the associative dictionary that contains both broadband and narrowband signals. The procedure of the proposed method is as follows: First, the collected datasets are analyzed by BMD and the composite multiscale fuzzy entropies (CMFEs) of the obtained effective components are calculated. Then, locality preserving projection (LPP) is applied for further feature extraction. Analysis results show BMD is more effective when drawing broadband feature from noise and BMD is adaptive for the quality monitoring of DPMIG welding.

Feature Extraction of Double Pulse Metal Inert Gas Welding Based on Broadband Mode Decomposition and Locality Preserving Projection