Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Has files: NoSubject: search.filters.subject.Fault detection

Search Results

Now showing 1 - 10 of 23
  • No Thumbnail Available
    Item
    A Time-Frequency Transform based Fault Detection and Classification Methodology for Transmission Lines
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ghatak, A.; Kishan, D.
    The inevitable events in power systems such as faults should be detected and resolved quickly to maintain system reliability. This paper proposes a Time-Frequency transform (wavelet transform) based fault detection and classification methodology using current signals. The Daubechies wavelet has been used to extract the features of the current signals. The proposed method detects a fault using the first level decomposition coefficients using wavelet transform, while the fault is classified by using the maximum values of the detail coefficients and logical analytical techniques. The proposed methodology is validated on a test model developed in the MATLAB Simulink environment. The performance of the proposed methodology has been verified under different fault configurations for different fault locations, resistances and inception times. The algorithm is also validated for a load change at the time of fault inception. The results show that the proposed methodology is accurate and reliable in fault detection and classification and can help in taking appropriate decisions to enhance the reliability of power system. © 2021 IEEE.
  • No Thumbnail Available
    Item
    Comprehensive study of mixed eccentricity fault diagnosis in induction motors using signature analysis
    (2012) Rajalakshmi Samaga, B.L.; Vittal, K.P.
    Modeling and simulation studies of an induction motor always help in identifying the parameter to characterize the asymmetrical fault in the machine. Hence in this paper, an air gap eccentric induction motor is modeled using multiple coupled circuit approach and 2D-Modified Winding Function Theory. The machine model is simulated under different eccentricity conditions to obtain the motor current spectra, power spectra and power factor spectra to detect the eccentricity related frequency components and the results are compared. All these analysis are based on the variation in the amplitude of mixed eccentricity related frequency component in these parameters with the variation in the level of eccentricity in the machine. A new fault severity detection method based on co-variance analysis is presented to predict the degree of deterioration in the health of the machine due to air gap eccentricity from the installation stage. © 2011 Elsevier Ltd. All rights reserved.
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    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).
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    Item
    Application and Evaluation of Random Forest Classifier Technique for Fault Detection in Bioreactor Operation
    (Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2017) Shrivastava, R.; Mahalingam, H.; Dutta, N.N.
    Bioreactors and associated bioprocesses are quite complex and nonlinear in nature. A small change in initial condition can greatly alter the output product quality. It is pretty difficult at times to model the system mathematically. In this work, the fault detection problem is studied in the context of bioreactors, mainly, a reactor from the penicillin production process. It is very important to identify the faults in a live process to avoid product quality deterioration. We have focused on the process history-based methods to identify the faults in a bioreactor. We want to introduce random forest (RF), a powerful machine learning algorithm, to identify several types of faults in a bioreactor. The algorithm is simple, easy to use, shows very good generalization ability without compromising much on the classification accuracies, and also has an ability to give variable importance as a part of the algorithm output. We compared its performance with two popular methods, namely support vector machines (SVM) and artificial neural networks (ANN), and found that the overall performance is superior in terms of classification accuracies and generalization ability. © 2017, Copyright © Taylor & Francis Group, LLC.
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    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..