Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system |
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| Affiliation: | 1. College of Safety Science & Engineering, Liaoning Technical University, Huludao 125105, China;2. Key Laboratory of Mine Thermo-motive Disaster & Prevention, Ministry of Education, Huludao 125105, China;3. School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China;1. Department of Mathematics, Weifang University, Weifang 261061, Sandong, China;2. Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran;3. Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq;4. Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Iraq;1. Electrical & Computer Engineering Department, Tarbiat Modares University, Tehran, Iran;2. Faculty of Electrical & Computer Engineering, Tarbiat Modares University, Tehran, Iran;1. Department of Engineering, University of Cambridge, United Kingdom;2. Department of Technology, Illinois State University, United States;3. Department of Neuroscience, Physiology, and Pharmacology, University College London, United Kingdom;4. Laing O’Rourke Professor, Department of Engineering, University of Cambridge, United Kingdom;1. College of Mechanical & Electrical Engineering/ National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China |
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| Abstract: | Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation. |
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| Keywords: | Mine ventilation Fault diagnosis Machine learning Algorithm selection Windage alteration fault |
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