EMD and Envelope Spectrum Based Bearing Fault Detection

Li, Zhen Tao; Li, Hui

doi:10.4028/scientific5/amr.459.233

Cited by 1 publication

(1 citation statement)

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“…Several fault-state monitoring techniques have been proposed to improve robustness, reliability, and production capacity and reduce maintenance costs [4,8,[10][11][12][13][14][15][16][17][18][19] Lei et al proposed an intelligent classification method using WPT and EMD [10]. Dimensionless time-domain feature vectors were extracted from each of the original vibration signals and preprocessed to form a combined feature set.…”

Section: Related Workmentioning

confidence: 99%

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Uddin

Kang

Nguyen

et al. 2014

Mathematical Problems in Engineering

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This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

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