Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
10 results
Search Results
Item Fault diagnosis of single point cutting tool using spectrum, cepstrum and wavelet analysis(American Institute of Physics Inc. subs@aip.org, 2019) Aralikatti, S.S.; Ravikumar, K.N.; Kumar, H.Tool fault diagnosis is essential in modern machining process to commemorate automation and precise manufacturing with limited human intervention. Automation increases productivity and efficient job handling ability. Online tool condition monitoring enables the fault diagnosis of cutting tool. A sensor is employed to acquire the information of the tool condition. The sensor data will be in raw form, which need to be processed using signal processing technique to derive the useful information about the tool fault. In the present work, a single point cutting tool of carbide tip used to machine oil hardened nickel steel. Various tool conditions are considered namely healthy, extended overhang, worn flank and broken tool. Vibration signals corresponding to each tool conditions are acquired using accelerometer to monitor tool condition. The time domain signals are transformed to frequency domain by employing fast Fourier transform (FFT). Other signal processing techniques such as cepstrum analysis and wavelet analysis used to understand the ailment of tool. The study also addresses limitations of signal processing tools and the advantage of one over the other. © 2019 Author(s).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 Application of vibration analysis and data mining techniques for bearing fault diagnosis in two stroke IC engine gearbox(American Institute of Physics Inc. subs@aip.org, 2020) Ravikumar, K.N.; Kumar, H.; Gangadharan, K.V.This paper is about monitoring of ball bearing used in the IC engine gearbox using condition monitoring techniques. Experiments are conducted on two stroke IC engine which is driven by the 3HP DC motor. Vibration signals are acquired from the gearbox with triaxial accelerometer. Ball bearing with good and induced faulty (outer race fault, inner race fault, ball fault, inner and outer race fault) conditions were used in the analysis. Fault diagnosis of the ball bearing has been carried out using data mining (DM) techniques. In DM there are three stages viz.; feature extraction, feature selection and feature classification. For all the conditions of bearing, statistical and empirical mode decomposition (EMD) features are extracted from the vibration signals. Decision tree technique (J48 algorithm) is used in the analysis for selecting significant features from the feature vector. From the chosen features, ball-bearing conditions are classified using random forest algorithm. Results obtained from the different classifiers were compared, and a better classification algorithm with a decision tree will be suggested for condition monitoring of the rotating components. © 2020 Author(s).Item Comparison and Feasibility Study of Hexanol/Diesel/Pongamia Biodiesel Blend on Engine Characteristics of a Common Rail Direct Injection Diesel Engine(SAE International, 2024) Santhosh, S.; Shahapur, S.; Kumar, G.N.; Ravikumar, K.N.; Raghavendra Reddy, N.V.In this work, the impact of hexanol/diesel/biodiesel blend on engine characteristics of a common rail direct injection (CRDI) diesel engine was studied. Biodiesel is more viscous in nature and higher cetane count, hexanol has a lower viscosity and cetane count. The drawbacks of both biodiesel and hexanol can be overcome by blending both hexanol and biodiesel with diesel fuel in the right proportion. Tests were carried out using a 4-stroke CRDI engine with two cylinders. Biodiesel and 1-hexanol were blended in a ratio of 10% each by volume with diesel and compared with B10D90 and B20D80 blends. It was noted that the addition of hexanol enhances the combustion characteristics of the engine. At 20% load H10B10D80 showed71.34 bar which is highest compared to other fuels in the test. The blends had a positive effect on emissions, there was drastic reduction in NOx was noticed, also HC and CO emission was lower than diesel emissions. The lowest CO, and HC emission is obtained for H10B10D80, which is 66%, 92% lower at 60% load compared to baseline readings. However, the blend had a slight negative effect on performance in contrast to diesel. The higher latent heat of vaporization of hexanol led to low temperature combustion contributing to the lowest NOx emissions. The combination of both hexanol and Pongamia biodiesel with diesel showed an effective reduction in greenhouse gases. Which will also reduce the dependency on fossil fuels. The lower carbon content of 1-hexanol contributes towards carbon neutrality. Overall, the hexanol and biodiesel are sustainable alternatives to the diesel fuel. © 2024 SAE International. All rights reserved.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 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.Item Gearbox fault diagnosis based on Multi-Scale deep residual learning and stacked LSTM model(Elsevier B.V., 2021) Ravikumar, K.N.; Yadav, A.; Kumar, H.; Gangadharan, K.V.; Narasimhadhan, A.V.Fault diagnosis methods based on signal analysis techniques are widely used to diagnose faults in gear and bearing. This paper introduces a fault diagnosis model that includes a multi-scale deep residual learning with a stacked long short-term memory (MDRL-SLSTM) to address sequence data in a gearbox health prediction task in an internal combustion (IC) engine. In the MDRL-SLSTM network, CNN and residual learning is firstly utilized for local feature extraction and dimension reduction. The experiment is carried out on the gearbox of an IC engine setup, two datasets are used; one is from bearing and the other from 2nd driving gear of gearbox. To reduce the number of parameters, down-sampling is carried out on input data before giving to the architecture. The model achieved better diagnostic performance with vibration data of gearbox. Classification accuracy of 94.08% and 94.33% are attained on bearing datasets and 2nd driving gear of gearbox respectively. © 2021 Elsevier LtdItem Classification of gear faults in internal combustion (IC) engine gearbox using discrete wavelet transform features and K star algorithm(Elsevier B.V., 2022) Ravikumar, K.N.; Madhusudana, C.K.; Kumar, H.; Gangadharan, K.V.Vibration-based fault diagnosis is one of the widely used techniques for condition monitoring of the machines equipped with a gearbox. Severe operating conditions of gearbox result in gear tooth failure. To develop an effective fault diagnosis technique for the mechanical system, a machine learning approach is highly necessary and plays a vital role in the area of condition monitoring. This paper presents the vibration-based fault diagnosis of IC engine gearbox operating under actual running condition. An Eddy current dynamometer is used to apply the external load on the output shaft of the engine. Driving gear with healthy condition and progressive tooth defect conditions are considered for the analysis. The vibration signals of engine gearbox under various gear tooth conditions are measured. Discrete wavelet transform features are extracted from the vibration signals and more contributing features for classification are selected using decision tree algorithm. The Lazy based classifiers viz, k-nearest neighbour algorithm, K-star algorithm and locally weighted learning algorithm are used for classification. A comparative study of these classifiers is made using percentage of classification accuracy. The maximum classification accuracy of about 97.5% is achieved by the K-star algorithm. Based on the experimental results, K-star algorithm and discrete wavelet transform technique can be used for diagnosing the gear faults in IC engine gearbox using vibration signals. © 2021 Karabuk UniversityItem Fault diagnosis of antifriction bearing in internal combustion engine gearbox using data mining techniques(Springer, 2022) Ravikumar, K.N.; Aralikatti, S.S.; Kumar, H.; Kumar, G.N.; Gangadharan, K.V.Ball bearing failure are most common failure in rotating machinery, which can be catastrophic. Hence obtaining early failure warning along with precise fault detection technique is at most important. Early detection and timely intervention are the key in condition monitoring for long term endurance of machine components. The early research has used signal processing and spectral analysis extensively for fault detection however data mining with machine learning is most effective in fault diagnosis, the same is presented in this paper. The vibration signals are acquired for an output shaft antifriction bearing in a two-wheeler gearbox operated at various loading conditions with healthy and fault conditions. Data mining is employed for these acquired signals. Statistical, discrete wavelet and empirical mode decomposition are employed for feature extraction process and J48 decision tree for feature selection. Classification is carried out using K*, Random forest and support vector machine algorithm. The classifiers are trained and tested using tenfold cross validation method to diagnose the bearing fault. A comparative study of feature extraction and classifiers are done to evaluate the classification accuracy. The results obtained from K* classifier with wavelet feature yielded better accuracy than rest other classifiers with classification accuracy 92.5% for bearing fault diagnosis. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.Item Transfer Learning-Based Fault Diagnosis of Internal Combustion (IC) Engine Gearbox Using Radar Plots(John Wiley and Sons Ltd, 2024) S, S.; Srivatsan, B.; Sugumaran, V.; Ravikumar, K.N.; Kumar, H.; Mahamuni, V.S.Due to constant loads, gear wear, and harsh working conditions, gearboxes are subject to fault occurrences. Faults in the gearbox can cause damage to the engine components, create unnecessary noise, degrade efficiency, and impact power transfer. Hence, the detection of faults at an early stage is highly necessary. In this work, an effort was made to use transfer learning to identify gear failures under five gear conditions—healthy condition, 25% defect, 50% defect, 75% defect, and 100% defect—and three load conditions—no load, T1 = 9.6, and T2 = 13.3 Nm. Vibration signals were collected for various gear and load conditions using an accelerometer mounted on the casing of the gearbox. The load was applied using an eddy current dynamometer on the output shaft of the engine. The obtained vibration signals were processed and stored as vibration radar plots. Residual network (ResNet)-50, GoogLenet, Visual Geometry Group 16 (VGG-16), and AlexNet were the network models used for transfer learning in this study. Hyperparameters, including learning rate, optimizer, train-test split ratio, batch size, and epochs, were varied in order to achieve the highest classification accuracy for each pretrained network. From the results obtained, VGG-16 pretrained network outperformed all other networks with a classification accuracy of 100%. © © 2024 S. Naveen Venkatesh et al.