Faculty Publications
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Item Stability aspects of nonreshaped berm breakwaters with reduced armor weight(2008) Rao, S.; Subrahmanya, K.; Balakrishna Rao, B.K.; Chandramohan, V.R.The present work involves the investigation of the influence of wave height, wave period, water depth, and sea-ward slope on the stability, wave runup, and wave rundown of statically stable rubble-mound berm breakwater. The weight of armor stones used in the present study is 20% lighter than the weight that is required for a conventional breakwater, designed using Hudson formula for a wave height of 0.1 m in the model. In the present work models with a berm width of 0.6 m, at constant depth of 0.32 m from the seabed were tested. The damage to the breakwater model with the berm was compared with the results on a model without the berm using different armor weights. The variation of relative runup and rundown was found for different values of wave steepness and water depths in front of the structure. The damage to the breakwater, wave runup, and rundown for the structure with seaward slope 1:2 and 1:1.5 were compared. The investigation was carried out in the Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal. © 2008 ASCE.Item Fault diagnosis of helical gear box using decision tree through vibration signals(RAMS Consultants, 2013) Sugumaran, V.; Jain, D.; Amarnath, M.; Kumar, H.This paper uses vibration signals acquired from gears in good and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The descriptive statistical features were extracted from vibration signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using J48 decision tree algorithm. The paper also discusses the effect of various parameters on classification accuracy. © RAMS Consultants.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 Failure Analysis of Cast Tubular Specimens of Al–5Zn–1Mg While Processing at Room Temperature by Equal Channel Angular Pressing (ECAP)(Springer Science and Business Media, LLC, 2014) Valder, J.; Rijesh, M.; Surendranathan, A.O.The ECAP process is a promising technique for imparting large plastic deformation and breaking down the ingot cast structure without a resultant decrease in cross-sectional area. In the present study, the suitability of this technique for processing cast Al–5Zn–1Mg tubular specimens at room temperature has been investigated. Tubular specimens were extruded through an ECAP die with an angle of 150° between the two intersecting channels without a back pressure. Sand was used as a mandrel during pressing. The tubular specimens failed miserably in the first pass itself. A failure analysis was carried out using SEM, and cause for failure was determined. © 2014, ASM International.Item Bearing health condition monitoring: Wavelet decomposition(Indian Society for Education and Environment indjst@gmail.com, 2015) Shanmukha Priya, V.; Mahalakshmi, P.; Naidu, V.P.S.Background/Objectives: Condition monitoring is one of the important functions to be carried out in the maintenance of any machine. In condition monitoring, there are several techniques among which the most commonly used technique for rotating machines is the vibration analysis. Methods/Statistical analysis: Discrete Wavelet Transform is used to decompose the vibration signal into 9 levels. For each level, mean ±std (standard deviation) are computed for both approximated and detailed coefficients. Findings: Bearing data obtained from the bearing test rig of Case Western Reserve University are used to test the algorithm. The standard of coefficients in level to 3 shows distant classification of faults. The levels which show clear classification among the bearings are those frequency bands in which the characteristic defect frequencies of faults occur. It is inferred that, the wavelet decomposition classifies the ball defect clearly than the frequency domain methods. Application/Improvements: Wavelet based bearing health condition monitoring technique can be used for bearing fault diagnosis and it can be extended for prognosis.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 Condition monitoring of roller bearing by K-star classifier and K-nearest neighborhood classifier using sound signal(Tech Science Press sale@techscience.com, 2017) Sharma, R.K.; Sugumaran, V.; Kumar, H.; Amarnath, M.Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost of transducer used to acquire vibration signal (Accelerometer). This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is performed from acquired sound signals. Then two layer feature selection is done using J48 decision tree algorithm and random tree algorithm. These selected features were classified using K-star classifier and k-nearest neighborhood classifier and parametric optimization is performed to achieve the maximum classification accuracy. The classification results for both K-star classifier and k-nearest neighborhood classifier for condition monitoring of roller bearing using sound signals were compared. © Copyright 2017 Tech Science Press.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 Face milling tool condition monitoring using sound signal(Springer, 2017) Madhusudana, C.K.; Kumar, H.; Narendranath, S.This article presents the fault diagnosis of the face milling tool using sound signal. During milling, sound signals of the face milling tool under healthy and fault conditions are acquired. Discrete wavelet transform (DWT) features are extracted from the acquired sound signals. The support vector machine (SVM) technique is used to classify the face milling tool conditions using the extracted DWT features. Also, a comparison of classification efficiencies of different classifiers with respect to different features extraction methods is carried out. It is shown that, all extracted DWT features demonstrate better results than those obtained from selected statistical features and empirical mode decomposition features. The SVM technique is the best classifier as it has given an encouraging result in this study when compared to other classifiers, and it has provided 83% classification accuracy for the given experimental conditions and workpiece of steel alloy 42CrMo4. Hence, the SVM method and DWT technique can be put forward for the applications of condition monitoring of the face milling tool with sound signal. © 2017, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.
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