Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques
The harsh and hostile internal environment of semi-autogenous (SAG) mills renders real-time monitoring of some critical variables practically unmeasured. Typically, feed size fractions are known to cause mill fluctuations and impede the consistent processing behaviour of ores. There is, therefore, t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Powders |
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| Online Access: | https://www.mdpi.com/2674-0516/2/2/18 |
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| author | Kwaku Boateng Owusu William Skinner Richmond K. Asamoah |
| author_facet | Kwaku Boateng Owusu William Skinner Richmond K. Asamoah |
| author_sort | Kwaku Boateng Owusu |
| collection | DOAJ |
| description | The harsh and hostile internal environment of semi-autogenous (SAG) mills renders real-time monitoring of some critical variables practically unmeasured. Typically, feed size fractions are known to cause mill fluctuations and impede the consistent processing behaviour of ores. There is, therefore, the need for continuous monitoring of mill parameters for optimal operation. In this paper, an acoustic-based sensing method is employed to estimate, in real time, a snapshot of the different feed size fractions presented to a laboratory-scale SAG mill. Employing the MATLAB 2020b programme, the mill acoustic signal is processed using various transform techniques such as power spectral density estimate (PSDE) by Welch’s method, discrete wavelet transform (DWT), wavelet packet transform (WPT), empirical mode decomposition (EMD), and variational mode decomposition (VMD). Different fractional bandpowers are obtained from the PSDE spectrum, while the statistical root mean square values are further extracted from DWT, WPT, EMD, and VMD as feature vectors. The features are used as input features in different machine-learning classification algorithms for different mill feed size fractions predictions. The various transform techniques and feed size fraction predictions are evaluated using the various performance indicators obtained from the confusion matrix such as accuracy, precision, sensitivity and F1 score. The study showed that the acoustic signal feature extraction techniques used in conjunction with the Support Vector Machine (SVM), linear discriminant analysis (LDA), and ensemble with subclass discriminant machine learning algorithms demonstrated improved performance for predicting feed size variations. |
| first_indexed | 2024-03-11T02:00:13Z |
| format | Article |
| id | doaj.art-a6432784a7ba4e6abefbacdc6e991d87 |
| institution | Directory Open Access Journal |
| issn | 2674-0516 |
| language | English |
| last_indexed | 2024-03-11T02:00:13Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Powders |
| spelling | doaj.art-a6432784a7ba4e6abefbacdc6e991d872023-11-18T12:14:40ZengMDPI AGPowders2674-05162023-04-012229932210.3390/powders2020018Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction TechniquesKwaku Boateng Owusu0William Skinner1Richmond K. Asamoah2Future Industries Institute, STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaFuture Industries Institute, STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaFuture Industries Institute, STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaThe harsh and hostile internal environment of semi-autogenous (SAG) mills renders real-time monitoring of some critical variables practically unmeasured. Typically, feed size fractions are known to cause mill fluctuations and impede the consistent processing behaviour of ores. There is, therefore, the need for continuous monitoring of mill parameters for optimal operation. In this paper, an acoustic-based sensing method is employed to estimate, in real time, a snapshot of the different feed size fractions presented to a laboratory-scale SAG mill. Employing the MATLAB 2020b programme, the mill acoustic signal is processed using various transform techniques such as power spectral density estimate (PSDE) by Welch’s method, discrete wavelet transform (DWT), wavelet packet transform (WPT), empirical mode decomposition (EMD), and variational mode decomposition (VMD). Different fractional bandpowers are obtained from the PSDE spectrum, while the statistical root mean square values are further extracted from DWT, WPT, EMD, and VMD as feature vectors. The features are used as input features in different machine-learning classification algorithms for different mill feed size fractions predictions. The various transform techniques and feed size fraction predictions are evaluated using the various performance indicators obtained from the confusion matrix such as accuracy, precision, sensitivity and F1 score. The study showed that the acoustic signal feature extraction techniques used in conjunction with the Support Vector Machine (SVM), linear discriminant analysis (LDA), and ensemble with subclass discriminant machine learning algorithms demonstrated improved performance for predicting feed size variations.https://www.mdpi.com/2674-0516/2/2/18semi-autogenous millacoustic sensingsupervised machine learning classification algorithmsfeature extractionmill feed size fraction |
| spellingShingle | Kwaku Boateng Owusu William Skinner Richmond K. Asamoah Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques Powders semi-autogenous mill acoustic sensing supervised machine learning classification algorithms feature extraction mill feed size fraction |
| title | Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques |
| title_full | Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques |
| title_fullStr | Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques |
| title_full_unstemmed | Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques |
| title_short | Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques |
| title_sort | acoustic sensing and supervised machine learning for in situ classification of semi autogenous sag mill feed size fractions using different feature extraction techniques |
| topic | semi-autogenous mill acoustic sensing supervised machine learning classification algorithms feature extraction mill feed size fraction |
| url | https://www.mdpi.com/2674-0516/2/2/18 |
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