Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
2 results
Search Results
Item 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.Item Ball bearing fault diagnosis based on vibration signals of two stroke ic engine using continuous wavelet transform(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Ravikumar, K.N.; Madhusudana, C.K.; Kumar, H.; Gangadharan, K.V.Ball bearings are used in the different critical fields of engineering applications such as IC engine, centrifugal pump and fans. In IC engine, the ball bearing is one of the critical components and it takes various types of dynamic loads and stresses. Condition monitoring of such ball bearing is very significant to avoid the catastrophic failure of rotating components in IC Engine. This article describes the fault detection of roller ball bearing of an IC engine gearbox with the use of signal processing technique such as spectrum analysis and Continuous Wavelet Transform (CWT) analysis. Vibration signals of IC engine are used to identify the fault in the ball bearing and to detect the healthy and fault bearing conditions. © Springer Nature Singapore Pte Ltd 2020.