Comparative analysis of PCA and LDA

Borade, Sushma Niket; Adgaonkar, Ramesh P.

doi:10.1109/icbeia.2011.5994243

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…A potent machine learning method called LDA can be used for both classification and dimensionality reduction. LDA is especially useful for jobs like facial recognition where it is necessary to compare data from various sources [17]. This method ignores all of the valuable data the second feature offers.…”

Section: Lda Algorithmmentioning

confidence: 99%

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Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines

et al. 2023

Meas. Sci. Technol.

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The predictive maintenance approach in the fault diagnosis of rotating machines is becoming important in industries in order to minimize operational cost and to improve the reliability of machines. Recent studies have focused on developing an effective prediction model to detect machinery faults at various speeds using machine learning techniques. In this research study, machine-learning-based models are developed to detect multi-component faults such as gear faults, bearing faults, and unbalanced shafts, by training the four speed data sets using decision trees, support vector machines and artificial neural networks. The machine learning models are improvised by fusing the vibration signals (X, Y and Z) and sound signals with the feature selection algorithm, minimal redundancy-maximum relevance (mRMR). The research work is extended by training a model with any three speeds and testing the trained model with the remaining speed. The state-of-the-art algorithms used to train all four speeds yield poor performance in the prediction of faults at testing speeds. A linear and quadratic discriminant analysis (QDA) is chosen based on its multivariate discrimination capability for better fault prediction at testing speed. The proposed combination of QDA with mRMR selection of fused vibration and sound feature set perform well in the detection of multiple faults at variable speeds.

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Section: Lda Algorithmmentioning

confidence: 99%

“…The concept of sensory data fusion has provided better performance in fault diagnosis from the results of experiments conducted [16]. The two most used dimensionality reduction approaches, principal component analysis (PCA) and linear discriminant analysis (LDA), are compared [17]. Unlike PCA, LDA can be used as a classifier algorithm.…”

Section: Introductionmentioning

confidence: 99%