Classification of power quality events using wavelet packet transform and extreme learning machine

Online tool wear prediction is a determining factor to the success of smart manufacturing operations. The implementation of sensors based Prognostic and Health Management (PHM) system plays an important role in estimating Remaining Useful Life (RUL) of cutting tools and optimizing the usage of Computer Numerically Controlled (CNC) machines. The present paper deals with health assessment and RUL estimation of the cutting tool machines based on Wavelet Packet Transform (WPT) and Extreme Learning Machine (ELM). This approach is done in two phases: a learning (offline) phase and a testing (online) phase. During the first phase, the WPT is used to extract the relevant features of raw data computed in the form of nodes energy. The extracted features are then fed to the learning algorithm ELM in order to build an offline model. In the online phase, the constructed model is exploited for assessing and predicting the RUL of cutting tool. The main idea is that ELM involves nonlinear regression in a high dimensional feature space for mapping the input data via a nonlinear function to build a prognostics model. The method was applied to real world data gathered during several cuts of a milling CNC tool. The performance of the proposed method is evaluated through the accuracy metric. Results showed the significance performances achieved by the WPT and ELM for early detection and accurate prediction of the monitored cutting tools.

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Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

International Transactions on Electrical Energy Systems ◽

10.1002/etep.2519 ◽

2018 ◽

Vol 28 (4) ◽

pp. e2519 ◽

Cited By ~ 14

Author(s):

Manish Kumar Saini ◽

Rajender Kumar Beniwal

Keyword(s):

Power Quality ◽

Extreme Learning Machine ◽

Integrated System ◽

Fast Time ◽

Power Quality Disturbances ◽

Learning Machine

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Detection and classification of power quality event using wavelet transform and weighted extreme learning machine

2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT) ◽

10.1109/iccpct.2016.7530177 ◽

2016 ◽

Cited By ~ 3

Author(s):

Mrutyunjaya Sahani ◽

Siddharth Mishra ◽

Ananya Ipsita ◽

Binayak Upadhyay

Keyword(s):

Wavelet Transform ◽

Power Quality ◽

Extreme Learning Machine ◽

Weighted Extreme Learning Machine ◽

Learning Machine

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Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2016-0097 ◽

2018 ◽

Vol 63 (4) ◽

pp. 383-394 ◽

Cited By ~ 5

Author(s):

Rajkumar Palaniappan ◽

Kenneth Sundaraj ◽

Sebastian Sundaraj ◽

N. Huliraj ◽

S.S. Revadi

Keyword(s):

Extreme Learning Machine ◽

Heart Diseases ◽

Wavelet Packet ◽

Statistical Significance ◽

Wavelet Packet Transform ◽

Medical Procedure ◽

Breath Sounds ◽

Pulmonary Pathology ◽

Learning Machine ◽

Elm Classifier

Abstract Background: Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. Methods: Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier. Results: The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively. Conclusion: The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

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