TY - CHAP
T1 - Linear Subspace Learning
AU - Ahmed, Hosameldin
AU - Nandi, Asoke
PY - 2019/12/6
Y1 - 2019/12/6
N2 - Various techniques have been proposed to learn subspace features from a large number of vibration signals in rotating machine fault diagnosis. These include linear subspace learning (LSL), nonlinear subspace learning, and feature‐selection techniques. LSL has been extensively used in several areas of information processing, such as data mining, dimensionality reduction, and pattern recognition. The basic idea of LSL is to map a high‐dimensional feature space to a lower‐dimensional feature space through linear projection. This chapter presents LSL techniques that can be used to learn features from a large amount of vibration signals. The techniques include: principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA), canonical correlation analysis (CCA), and partial least squares (PLS). Of these techniques, PCA, ICA, and LDA are amongst the most commonly used in machine fault diagnosis. CCA and PLS have been considered in many application of fault detection including machine fault detection.
AB - Various techniques have been proposed to learn subspace features from a large number of vibration signals in rotating machine fault diagnosis. These include linear subspace learning (LSL), nonlinear subspace learning, and feature‐selection techniques. LSL has been extensively used in several areas of information processing, such as data mining, dimensionality reduction, and pattern recognition. The basic idea of LSL is to map a high‐dimensional feature space to a lower‐dimensional feature space through linear projection. This chapter presents LSL techniques that can be used to learn features from a large amount of vibration signals. The techniques include: principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA), canonical correlation analysis (CCA), and partial least squares (PLS). Of these techniques, PCA, ICA, and LDA are amongst the most commonly used in machine fault diagnosis. CCA and PLS have been considered in many application of fault detection including machine fault detection.
KW - Principal component analysis
KW - Vibrations
KW - Feature extraction
KW - Covariance matrices
KW - Wavelet transforms
KW - Fault diagnosis
KW - Matrix decomposition
UR - https://ieeexplore.ieee.org/document/8958824/
UR - https://onlinelibrary.wiley.com/doi/10.1002/9781119544678.ch7
U2 - 10.1002/9781119544678.ch7
DO - 10.1002/9781119544678.ch7
M3 - Chapter
SN - 9781119544623
SN - 9781119544678
SP - 131
EP - 151
BT - Condition Monitoring with Vibration Signals
ER -