Journal Articles
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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.Item Condition monitoring of single point cutting tools based on machine learning approach(International Institute of Acoustics and Vibrations P O Box 13 Auburn AL 36831, 2018) Gangadhar, N.; Kumar, H.; Narendranath, S.; Sugumaran, V.This paper presents the use of multilayer perceptron (MLP) for fault diagnosis through a histogram feature extracted from vibration signals of healthy and faulty conditions of single point cutting tools. The features were extracted from the vibration signals, which were acquired while machining with healthy and different worn-out tool conditions. Principle component analysis (PCA) used to select important extracted features. The artificial neural network (ANN) algorithm was applied as a fault classifier in order to know the status of cutting tool conditions. The accuracy of classification with MLP was found to be 82.5 %, which validates that the proposed approach is an effective method for fault diagnosis of single point cutting tools. © 2018 International Institute of Acoustics and Vibrations. All Rights Reserved.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 Fault diagnosis of single-point cutting tool using vibration signal by rotation forest algorithm(Springer Nature, 2019) Aralikatti, S.S.; Ravikumar, K.N.; Kumar, H.In various machining operations, the tool condition monitoring (TCM) is highly necessary to avoid uncertain downtime in production. TCM provides continuously the condition of cutting tool by noticing various parameters such as temperature, acoustic emission and vibration. One of the best ways to monitor the condition of cutting tools for unmanned machining is by observing tool vibration signature. In the present work, vibration signals are acquired from the cutting tool. One healthy state and three faulty conditions of tools are considered for the study. The faulty tools considered in the current study are worn flank, broken tool and extended overhang. The vibration signals of these faulty tool conditions are used to train the machine learning algorithm. Statistical features are extracted from the vibration signal to feed as input to the J48 decision tree. The classifier algorithm used in the current study is rotation forest algorithm. The algorithm uses only significant features which are selected from a decision tree. The algorithm is validated with test dataset to recognize the faulty or healthy state of the tool. It was found that the algorithm could classify the tool condition with 95.00% classification accuracy. © 2019, Springer Nature Switzerland AG.Item Characterization of Fault Signature Due to Combined Air-Gap Eccentricity and Rotor Faults in Induction Motors(Praise Worthy Prize S.r.l, 2021) Bindu, S.; Sumam David, S.; Thomas, V.V.An accurate means of non-invasive condition monitoring of the popular industrial drive, three-phase squirrel-cage induction motor, can help to avoid unscheduled maintenance downtime and loss. Faults like air-gap eccentricity can exist even in a newly assembled drive and hence may co-exist with other internal defects. Despite it being a possible situation, the occurrence of simultaneous faults has seldom been studied. Therefore, there is a need for identifying fault signatures of combined fault conditions in a non-invasive manner. This paper presents a detailed model-based study on a three-phase squirrel-cage induction motor with the simultaneous existence of broken rotor-bar and air-gap mixed eccentricity faults using spectral analysis of stator current, instantaneous power, and estimated air-gap torque signals. The modelling of the machine is done using the Multiple Coupled Circuit method and modified to model the presence of the combined fault conditions. A comparative evaluation with various fault conditions and their severity is carried out by spectral analysis, and unique slip-dependent frequency components are identified in the spectra of diagnostic signals. This fault characterization is the most significant contribution of this paper. © 2021 Praise Worthy Prize S.r.l.-All rights reserved.