A feature extraction for latent fault detection and failure modes classification method of board-level package subjected to vibration loadings is presented for prognostics and health management(PHM) of electronics using adaptive spectrum kurtosis and kernel probability distance clustering. First, strain response data of electronic components is filtered by empirical mode decomposition(EMD) method based on maximum spectrum kurtosis(SK), and fault symptom vector is developed by computing and reconstructing the envelope spectrum. Second, nonlinear fault symptom data is mapped and clustered in sparse Hilbert space using Gaussian radial basis kernel probabilistic distance clustering method. Finally, the current state of board level package is estimated by computing the membership probability of its envelope spectrum. The experimental results demonstrated that the method can detect and classify the latent failure mode of board level package effectively before it happened. A feature extraction for latent fault detection and failure modes classification board-level package subjected to vibration loadings is presented for prognostics and health management (PHM) of electronics using adaptive spectrum kurtosis and kernel probability distance clustering. First, strain response data of electronic components is filtered by empirical mode decomposition (EMD) method based on maximum spectrum kurtosis (SK), and fault symptom vector is developed by computing and reconstructing the envelope spectrum. Second, nonlinear fault symptom data is mapped and clustered in sparse Hilbert space using Gaussian Finally, the current state of board level package is estimated by computing the membership probability of its envelope spectrum. The experimental results of the method can detect and classify the latent failure mode of board level package effectively it happened.