Mental task classification using wavelet transform and support vector machine

Kshirsagar, Pravin R.; Joshi, Kirti; Hendre, Vaibhav; Paliwal, K.K.; Akojwar, Sudhir G.; Atauurahman, Sanaurrahman

doi:10.1504/ijbet.2021.120191

Cited by 1 publication

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“…Among the machine learning methods, SVM, with powerful generalization ability and high learning efficiency, has been widely used as a fault detection model for bearings [ 24 , 25 , 26 ], motors [ 27 ], gearboxes [ 28 , 29 ], and pumps [ 30 ]. Improved algorithms for SVM include the wavelet transform-based SVM [ 31 ], least squares-based SVM [ 32 , 33 ], hyper-sphere-structured SVM [ 34 ], proximal SVM [ 35 ], etc. In addition, feature selection in learning has recently emerged as a crucial issue [ 36 , 37 ].…”

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confidence: 99%

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps

Xia

Xiang

2022

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A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a crucial issue. Therefore, piston wear detection and feature selection are essential and urgent. In this paper, we propose a vibration signal-based methodology using the improved spare support vector machine, which can integrate the feature selection into the piston wear detection learning process. Forty features are defined to capture the piston wear signature in the time domain, frequency domain, and time–frequency domain. The relevance and impact of sparsity in 40 features are illustrated through the single and multiple statistical feature analysis. Model performance is assessed and the sparse features are discovered. The maximum model testing and training accuracy are 97.50% and 96.60%, respectively. Spare features s10, s12, Ew(8), x7, Ee(5), and Ee(4) are selected and validated. Results show that the proposed methodology is applicable for piston wear detection and feature selection, with high model accuracy and good feature sparsity.

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