Bearing performance degradation assessment using locality preserving projections

By Jianbo Yu. Expert systems with applications, 2011

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An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017, methodology): “16-19, LPP1 and LPP2 represent the first two projected vectors obtained by LPP, PCA1 and PCA2 denote the first two principle components given by PCA, and the annotations 1-7 corresponding to the condition labels listed in Table 1.”
Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, methodology): “Instead of the exponential weighted moving average, Yu [112] used a Gaussian mixture model to model the low-dimensional feature space obtained by locality-preserving projections.”
Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis (2015, background): “No feature selection at all is used in [5], [6], [9], [10], [12]–[16], [18], and [19] or only univariate methods, i.”
Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “[86] extracted HIs using the locality preserving projection approach and calculated their confidence bounds as the alarm thresholds using the kernel density estimation approach.”