Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analytical approach to similarity-based prediction of manufacturing system performance
Computers in Industry
Expert Systems with Applications: An International Journal
Global geometric similarity scheme for feature selection in fault diagnosis
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The sensitivity of various original features that are characteristics of bearing performance may vary significantly under different working conditions. Thus it is critical to devise a systematic approach that provides a useful and automatic guidance on extracting the most effective information from the original features generated from vibration signals for bearing performance degradation assessment without human intervention. This paper proposed a locality preserving projections (LPP)-based dimension reduction and feature extraction (FE) approach. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP is capable to discover local structure of the data manifold. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional feature set compared with PCA. By using the extracted information by LPP, a multivariate statistical process control (MSPC)-based bearing performance quantification index is proposed, where an Exponential Weighted Moving Average (EWMA) statistic is developed by combining two effective statistic T^2 and squared prediction error (SPE) statistics. LPP-EWMA does not need too much prior knowledge to improve its utility in real-world applications. The effectiveness of LPP-EWMA is evaluated experimentally on bearing test-beds. The experimental results indicate its potential applications as an effective and simple tool for bearing performance degradation assessment.