... signifies the fault inception which led to failure stage promptly. The monotonicity metric is utilized to select the optimal feature set that best represents bearing degradation. Principal component analysis (PCA) is used for dimension reduction and fusion, and a unidimensional health indicator (HI...

... that are influenced by the samples’ material state. Principal component analysis (PCA) is applied to reduce the dimensionality of feature data and extract higher order features. Afterward, probabilistic neural network (PNN) classifies the sample based on the percentage fatigue life to discover the most correlated MBN...

... segregation using principal component analysis (PCA). In the second phase, the embedding dimension was reduced through empirical mode decomposition (EMD). The embedding parameters were derived using singular system analysis (SSA) and average mutual information function (AMIF). Based, on Takens theorem...