Bearing fault diagnosis based on independent component analysis and optimized support vector machine

Thelaidjia, Tawfik; Moussaoui, Abdelkrim; Chenikher, Salah

doi:10.1109/icmic.2015.7409362

Cited by 4 publications

(1 citation statement)

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“…Optionally, the feature selection can be employed to select only the most discriminant features from the original feature set. Principal component analysis (PCA) [9], independent component analysis (ICA) [10], sequential selection [6], and Fisher discriminant analysis (FDA) [11] are some representative techniques applied in feature selection. The final feature set is fed into a classifier to predict the condition of the input signal.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

Hoang

Tran

Van

2021

Sensors

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This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.

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