Existing signal decomposition algorithms suffer from mode mixing, decomposition instability, and poor noise resistance, hindering subsequent feature extraction and fault diagnosis. To address these issues, this paper proposes a spectral distribution decomposition (SDD) algorithm based on the spectral probability density function (SPDF) for gearbox fault diagnosis. The proposed SDD employs a spectral distribution strategy utilizing maximum broadcasting and Gaussian kernel convolution to transform raw spectra into the SPDF, which characterizes signal distribution properties. Integrating the SPDF with the expectation-maximization (EM) algorithm, SDD decomposes signals into multiple distribution mode functions (DMFs) with well-defined frequency boundaries and a residual signal. For gearbox fault diagnosis, an adaptive filtering strategy incorporating SDD is developed to extract fault-related frequency bands and diagnosis indicators. Comparative simulations with well-known decomposition techniques, together with experimental tests on the fault diagnosis of laboratory gearboxes and actual wind turbine gearboxes, demonstrate the effectiveness and superiority of the proposed approach.
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