Faculty Publications
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Publications by NITK Faculty
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Item An FPGA based embedded sytems for online monitoring and power management in a standalone micro-grid(wiley, 2022) Dastagiri Reddy, B.D.; Venkatraman, K.; Selvan, M.P.; Sridharan, S.The amalgamation of numerous renewable sources to fulfill the energy demands of the consumers in distant areas with better reliability is a significant challenge because of unpredictable and fluctuating nature of renewable sources. Hence, An effective micro-grid control is required to accomplish the power balance among the load demand and generated power. In this work, A Field Programmable Gate Array (FPGA) based embedded system for online monitoring and power management is proposed for a standalone micro-grid. The developed standalone micro-grid consists of wind energy and solar energy as renewable sources, Diesel generator as a backup supply, And battery bank as storage unit. An algorithm is developed using two Field Programmable Gate Array (FPGA) controllers namely source FPGA controller and load FPGA controller implemented as an embedded system connected through an Ethernet interface with separate source control and load control features. The load FPGA controller provides load management (connecting/ disconnecting non-critical loads) based on the available power and load demand. The source FPGA controller provides source management by controlling the renewable energy sources and dump load. Finally, To monitor the electrical parameters and statuses of various energy sources (wind and solar), Loads (critical and noncritical loads) and storage unit (battery bank) an online monitoring system is developed without the need of a dedicated personal computer. This work has been developed in such a way that it is replicable on the large scale in the field. © 2022 Scrivener Publishing LLC. All rights reserved.Item Electrical fault detection in three phase squirrel cage induction motor by vibration analysis using MEMS accelerometer(2005) Maruthi, G.S.; Vittal, K.P.The ever increasing applications of 3phase Induction motor have resulted in a growing awareness on addressing various performance demands. The condition monitoring of these electrical machines has received considerable attention in recent years. The diagnostics based on vibrations produced by these motors can form valuable data for preventive maintenance of these machines. The vibration analysis demands appropriate vibration transducer. With the advent of MEMS [Micro Electro Mechanical Systems], Technology I.C. chip mounted accelerometers are available. These accelerometers are having merits of low cost, high reliability, and low power consumption when compared to piezoelectric types which are conventionally used earlier. These accelerometers are gaining wide acceptance in condition monitoring of electrical machines. This paper presents the instrumentation developed around MEMS accelerometer and also proposes a technique of detecting abnormal electrical operating conditions in 3phase Induction motor such as single phasing, voltage unbalance by employing spectrum analysis of vibrations measured through MEMS accelerometer. © 2005 IEEE.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.Item A Suspended Polymeric Microfluidic Sensor for Liquid Condition Monitoring(International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII, 2022) Oseyemi, A.E.; Sedaghati, R.; Chandramohan, S.; Kumar, H.; Packirisamy, M.The measurability of fluid properties like density and viscosity comes with a huge potential in numerous sensing applications, ranging from physical to biological to chemical. A vital quality of a lubricant is its viscosity. In general, liquids with high viscosity have molecules with higher cohesion capacity (higher flow resistance) while those with low viscosity have less cohesion ability, allowing for higher flow rates. This makes viscosity an essential indicator in condition monitoring programs, as information about the cohesive strength of the layers of a liquid can allow us to assess the liquid's ability to form a physical barrier between moving parts. This study proposes a microcantilever-based microfluidic platform that leverages the interaction between cal barrier flow and the bending characteristics of the beam for high sensitivity detection of changes in fluid properties, such as dynamic viscosity, density, and kinematic viscosity, from which valuable information about the health of structures engaging the liquid can be obtained. © 2022 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.Item Condition Monitoring of Submodule Capacitors in Modular Multilevel Converters—A Review(Elsevier Ltd, 2025) Saravanakumar, R.; Sivakumar, N.; Devi, V.S.K.; Shanthini, C.; Jena, D.; Ibaceta, E.; Diaz-D, M.; Rodríguez, J.Modular Multilevel Converters are highly promising power converter technologies used in high-voltage and high-power applications. The applications of modular multilevel converters are being increased in various industrial and renewable energy sectors due to their superior performance and efficiency. The modular multilevel converters contain multiple submodule capacitors, and these capacitors are the fragile components. The operating conditions and performance of these capacitors directly influence the system's reliability and operation. Hence, condition monitoring schemes are essential for submodule capacitors to ensure and enhance the modular multilevel converters operation which consequently reduces unscheduled maintenance. This article provides a detailed review and comprehensive analysis of condition monitoring schemes for submodule capacitors in modular multilevel converters. The review classifies the existing condition monitoring schemes into four major groups and thirteen subgroups and analyzes their methodologies using advantages and limitations of each scheme. Further, a critical analysis is presented with five significant parameters used to evaluate the condition monitoring schemes. The review highlighted the challenges related to condition monitoring accuracy, cost-effectiveness and system architecture that are to be studied in future. © 2025 Elsevier LtdItem 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 Fault diagnosis of single point cutting tool through discrete wavelet features of vibration signals using decision tree technique and multilayer perceptron(Krishtel eMaging Solutions Pvt. Ltd, 2017) Gangadhar, N.; Vernekar, K.; Kumar, H.; Narendranath, S.Tool condition monitoring in machining plays a crucial role in modern manufacturing systems, finding state of the tool wear in early with the help of condition monitoring system will reduce downtime and excessive power drawing while machining. Vibration analysis of mechanical systems can be used to identify the tool condition to distinguish good or worn tool. In this study, vibration signals were acquired during turning operation, fault diagnosis using machine learning techniques has been carried out with new and different type of worn-out tool inserts. Discrete Wavelet Features (DWT) were extracted from acquired vibration signal for various cutting tool conditions using MATLAB. Most significant features were selected out of extracted discrete wavelet features using decision tree technique (J48 algorithm). Multilayer perceptron has been used as a classifier, selected features were given as input for the classifier. The classification accuracy with multilayer perceptron was found to be 96%. © KRISHTEL eMAGING SOLUTIONS PVT. LTD.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 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.