Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Fault diagnosis

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    Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal
    (Elsevier Ltd, 2018) Madhusudana, C.K.; Kumar, K.; Narendranath, S.
    The monitoring of machining process can improve the quality of product and economy of production. The monitoring system helps to recognize and monitor the surface roughness, dimensional tolerance and tool condition. In this way, the condition monitoring system provides precise dimensional products, high productivity and enhanced machine tool life. This paper presents the classification of healthy and faulty conditions of the face milling tool using Decision tree (J48 algorithm) technique through machine learning approach. The sound signals of the face milling tool under healthy and faulty conditions are acquired. A set of discrete wavelet features are extracted from the sound signals using discrete wavelet transform (DWT) method. Decision tree technique is used to select prominent features out of all extracted features. The selected features are fed to the same algorithm for classification. Output of the algorithm is used to study and categorize the tool conditions. The decision tree model has provided a good classification accuracy of about 81% for the given sound signals and can be considered for fault diagnosis/condition monitoring. From the experimental results, it is suggested that the proposed method which comprises of decision tree and DWT techniques with sound signals can be recommended for the applications of fault diagnosis of the face milling tool. © 2017 Elsevier Ltd. All rights reserved.
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    Helical Gearbox Fault Diagnosis Using Adaptive Artificial Neural Network and Adaptive Coyote Optimization
    (Institute of Electrical and Electronics Engineers Inc., 2023) Bokil, P.P.; Joladarashi, S.; Kadoli, R.; Chavan, P.; Bhangale, R.
    The Helical gearboxes (HG) are considered a significant part of providing power transmission of manufacturing administrations and are exposed to numerous failures because of their extended and intensive situation of acceleration. Therefore, to enhance the security and dependency of the HGs, monitoring the health condition and detecting different types of failures is essential. The estimation of HG failure detection majorly includes electric signals, the noise produced by airborne, lubricant examination, thermal images, and so on. Therefore, this research proposes an Adaptive Coyote Optimization-Adaptive Artificial Neural Network (A2CO-ANN) Gearbox fault diagnosis and missing data imputation for preventing the loss of significant data values. Moreover, the comparative analysis of the A2CO-ANN technique is examined using the available datasets DTS1 and DTS2 with the existing classifiers like Random Forest (RF), K-Nearest Neighbors (KNN), Decision tree (DT), Fuzzy, as well as Adaptive ANN is examined in terms of the performance metrics. Thus, the accuracy of the A2CO-ANN method on training percentage 80 for DTS1 and DTS2 is 91.54% and 90.05%, whereas the sensitivity rate is estimated as 98.26% and 98.35%, as well as the specificity rate, is valued as 84.08% and 81.09% respectively, which is increased than the traditional methods. © 2023 IEEE.