Faculty Publications
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Item Fault diagnosis of deep groove ball bearing through discrete wavelet features using support vector machine(COMADEM International rajbknrao@btinternet.com, 2014) Vernekar, K.; Kumar, H.; Gangadharan, K.V.Bearings are the most important and frequently used machine components in most of the rotating machinery. In industry, breakdown of such crucial components causes heavy losses. So prevention of failure of such components is very essential. This paper presents an online fault detection of a bearing used in an internal combustion engine through machine learning approach using vibration signals of bearing in healthy and simulated faulty conditions. Vibration signals are acquired from bearing in healthy as well as different simulated fault conditions of bearing. The Discrete Wavelet Transform (DWT) features were extracted from vibration signals using MATLAB program. Decision tree technique (J48 algorithm) has been used for important feature selection out of extracted DWT features. Support vector machine is being used as a classifier and obtained results found with classification accuracy of 98.67%.The advantage of machine learning technique for fault diagnosis over conventional vibration analysis approach has demonstrated in this paper.Item Fault diagnosis studies of face milling cutter using machine learning approach(Multi-Science Publishing Co. Ltd claims@sagepub.com, 2016) Madhusudana, C.K.; Budati, S.; Gangadhar, N.; Kumar, H.; Narendranath, S.Successful automation of a machining process system requires an effective and efficient tool condition monitoring system to ensure high productivity, products of desired dimensions, and long machine tool life. As such the component's processing quality and increased system reliability will be guaranteed. This paper presents a classification of healthy and faulty conditions of the face milling tool by using the Naive Bayes technique. A set of descriptive statistical parameters is extracted from the vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features are fed to the Naive Bayes algorithm. The output of the algorithm is used to study and classify the milling tool condition and it is found that the Naive Bayes model is able to give 96.9% classification accuracy. Also the performances of the different classifiers are compared. Based on the results obtained, the Naive Bayes technique can be recommended for online monitoring and fault diagnosis of the face milling tool. © 2016 The Author(s).Item Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm(Springer India sanjiv.goswami@springer.co.in, 2017) Vernekar, K.; Kumar, H.; Gangadharan, K.V.This paper presents engine gearbox fault diagnosis based on empirical mode decomposition (EMD) and Naïve Bayes algorithm. In this study, vibration signals from a gear box are acquired with healthy and different simulated faulty conditions of gear and bearing. The vibration signals are decomposed into a finite number of intrinsic mode functions using the EMD method. Decision tree technique (J48 algorithm) is used for important feature selection out of extracted features. Naïve Bayes algorithm is applied as a fault classifier to know the status of an engine. The experimental result (classification accuracy 98.88%) demonstrates that the proposed approach is an effective method for engine fault diagnosis. © 2017, Indian Academy of Sciences.Item Use of discrete wavelet features and support vector machine for fault diagnosis of face milling tool(Tech Science Press sale@techscience.com, 2018) Madhusudana, C.K.; Gangadhar, N.; Kumar, H.; Narendranath, S.This paper presents the fault diagnosis of face milling tool based on machine learning approach. While machining, spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired. A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform (DWT) technique. The decision tree technique is used to select significant features out of all extracted wavelet features. C-support vector classification (C-SVC) and ?-support vector classification (?-SVC) models with different kernel functions of support vector machine (SVM) are used to study and classify the tool condition based on selected features. From the results obtained, C-SVC is the best model than ?-SVC and it can be able to give 94.5% classification accuracy for face milling of special steel alloy 42CrMo4. © © 2018 Tech Science Press..Item Engine gearbox fault diagnosis using machine learning approach(Emerald Group Publishing Ltd. Howard House Wagon Lane, Bingley BD16 1WA, 2018) Vernekar, K.; Kumar, H.; Gangadharan, K.V.Purpose: Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues. Design/methodology/approach: This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm. Findings: The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis. Originality/value: This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques. © 2018, Emerald Publishing Limited.Item Comparative study on tool fault diagnosis methods using vibration signals and cutting force signals by machine learning technique(Tech Science Press sale@techscience.com, 2020) Aralikatti, S.S.; Ravikumar, K.N.; Kumar, H.; Shivananda Nayaka, H.; Sugumaran, V.The state of cutting tool determines the quality of surface produced on the machined parts. A faulty tool produces poor surface, inaccurate geometry and non-economic production. Thus, it is necessary to monitor tool condition for a machining process to have superior quality and economic production. In the present study, fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique. Cutting force and vibration signals were acquired to monitor tool condition during machining. A set of four tooling conditions namely healthy, worn flank, broken insert and extended tool overhang have been considered for the study. The machine learning technique was applied to both vibration and cutting force signals. Discrete wavelet features of the signals have been extracted using discrete wavelet transformation (DWT). This transformation represents a large dataset into approximation coefficients which contain the most useful information of the dataset. Significant features, among features extracted, were selected using J48 decision tree technique. Classification of tool conditions was carried out using Naïve Bayes algorithm. A 10 fold cross validation was incorporated to test the validity of classifier. A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique. © 2020 Tech Science Press. All rights reserved.