Abstract
In Industry 4.0, Internet of Thing (IoT) plays a crucial role in solving maintenance problems of a wide range of household as well as industrial equipment. Machine Learning techniques are a vital part of IoT, which deals with an automatic prediction for predictive maintenance systems. ML allows automated fault diagnosis and prognosis for various types of equipment. In this study, Majority Voting Machine Learning approach demonstrated for fault diagnosis of Mechanical components such as rolling element bearing and gearbox with vibration data. Temporal statistical features are extracted from vibration data which are further used as input to ML techniques for training and testing purpose. For analysis, Majority Voting ensemble technique is made by five tuned base classifiers, i.e. Support Vector Machine, k-Nearest Neighbor, Naive Bayes, Multilayer Perceptron and Decision Tree. Results are carried-out for the individual base classifier and Majority Voting technique, and classification performance and accuracy found better with Majority Voting approach than accuracies of individual base classifiers.
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Patil, P.S., Patil, M.S., Tamhankar, S.G., Patil, S., Kazi, F. (2021). Majority Voting Machine Learning Approach for Fault Diagnosis of Mechanical Components. In: Gao, XZ., Kumar, R., Srivastava, S., Soni, B.P. (eds) Applications of Artificial Intelligence in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4604-8_55
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DOI: https://doi.org/10.1007/978-981-33-4604-8_55
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