Faculty Publications
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Item Performance and emission characteristics studies on stationary diesel engines operated with cardanol biofuel blends(2012) Mallikappa, D.N.; Pratap Reddy, R.P.; Murthy, C.S.N.This work composed with performance and emission studies of three stationary diesel engines operated with 20% cardanol biofuel volumetric blends. A single cylinder diesel engine and VCR engines were used to evaluate theperformance and emission characteristics of cardanol biofuel. An extended experimental study was conducted on a double cylinder CI engine, to evaluate the performance and emission characteristics. The cardanol biofuel volumetric blends between 0-25% and base fuel (Petro diesel) were tested at various loads between 0-full load. From the results, brake thermal efficiency, increased with increase in load. The brake specific energy consumption decreased by 30 to 40% with increase in brake power. The HC emissions were nominal up to B20, and more at B25. The NOx emissions (ppm) increased with increased proportion of blends. The carbon monoxide emissions increased with higher blends and decreased slightly at higher loads. From this investigation, it is observed that up to 20% blends of cardanol biofuels may be used in CI engines without any modifications.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 Fault Detection of Gear Using Spectrum and Cepstrum Analysis(Springer Nature, 2015) Vernekar, K.; Kumar, H.; Gangadharan, K.V.This paper presents an experimental investigation on damage detection of internal combustion (IC) engine gear box using conventional vibration spectrum and cepstrum analysis. Experiment was carried out on two stroke internal combustion engine gearbox without considering the combustion. Vibration signals were collected for healthy as well as defective gear condition. The signals were analysed in time domain, frequency domain and cepstrum plots for fault detection. An experimental result demonstrates the dynamic behaviour in frequency domain, which is dominated by gear mesh frequency (GMF) and its harmonics.Based on the experimental results obtained, spectrum and cepstrum analysis can be effectively used for fault prediction of machine components. © Printed in India.Item Influence of spark timing on the performance and emission characteristics of gasoline–hydrogen-blended high-speed spark-ignition engine(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2017) Shivaprasad, K.V.; Chitragar, P.R.; Nayak, V.; Kumar, G.N.This article experimentally investigates the effect of spark timing on performance and emission characteristics of high-speed spark-ignition (SI) engine operated with different hydrogen–gasoline fuel blends. For this purpose, the conventional carbureted SI engine is modified into an electronically controllable engine, wherein an electronically controllable unit was used to control the ignition timings and injection duration of gasoline. The tests were conducted with different spark timings at the wide open throttle position and 3000 rpm engine speed. The experimental results demonstrated that brake mean effective pressure and engine brake thermal efficiency increased first and then decreased with the increase in spark advance. Peak cylinder pressure, temperature and heat release rate were increased until 20% hydrogen addition and with increased spark timings. NOx emissions were continuously increased with the increment in both spark timings and hydrogen addition, whereas hydrocarbon emissions were increased with spark timings but decreased with hydrogen addition. CO emissions were reduced with the increase in spark timing and hydrogen addition. © 2016 Informa UK Limited, trading as Taylor & Francis Group.Item Optimized ANN-GA and experimental analysis of the performance and combustion characteristics of HCCI engine(Elsevier Ltd, 2018) Anarghya, A.; Rao, N.; Nayak, N.S.; Tirpude, A.R.; Harshith, D.N.; Samarth, B.R.HCCI (Homogeneous Charge Compression Ignition) engine has the benefit of operating at high thermal efficiency and low emissions of NOx and soot. However, it has challenges of complex combustion phase controlling and low operating range. This research work investigated the performance and combustion characteristics of HCCI engine with numerical simulations on ANSYS FLUENT and neural network models. The numerical and neural network results were validated by experimental observations with different fuel properties and reduced valve lifts for trapping of the exhaust gases. Experiments were performed on a SMART engine for different speeds and inlet air temperature, with various reference fuels (PRF30, PRF50, PRF70) and methanol to validate the CFD and ANN-GA observations. The engine performance was analyzed for IMEP, ISFC and thermal efficiency, which were found to be 8.2 bar, 205 g/kWh and 44.5% respectively as the optimum performance with PRF-70 fuel. The trapping of the residual gases was performed with various fuel blends in order to overcome the cyclic variations and to improve the operating zones near the knock boundary. The heat release rate was significantly reduced with trapped exhaust gases, and operating region was improved with the use of methanol fuel. Overall the trapping of the hot residual gases resulted in the maximum increase in the operating region by 12% and reduced cyclic variations by 15% for methanol fuel. The exhaust emissions were analyzed and ultra-low emissions of NOx at lean operating conditions were observed with the reduced valve lifts. The study results indicated thermal NO emissions on an average were decreased by 7.8%, CO emissions reduced by 6% and HC emissions increased by 9%. Methanol had ultra-low emissions of HC and CO, but higher emissions of NO and PRF30 had lower emissions of NO. However, ANN-GA model gave satisfactory combustion characteristics and emissions with respect to experimental results. Thus, CFD simulations, Neural Network methods and experimental study gave valuable thoughts of trapped residual gases approach on performance, combustion and emission characteristics of HCCI with PRF's and methanol fuel. © 2017 Elsevier LtdItem Fault diagnosis of internal combustion engine gearbox using vibration signals based on signal processing techniques(Emerald Group Holdings Ltd., 2020) Kn, R.; Kumar, H.; Gn, K.; Kv, G.Purpose: The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques. Design/methodology/approach: Vibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques. Findings: The obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions. Practical implications: The proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics. Originality/value: In this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries. © 2020, Emerald Publishing Limited.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 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 A comparative study of NOx mitigating techniques EGR and spark delay on combustion and NOx emission of ammonia/hydrogen and hydrogen fuelled SI engine(Elsevier Ltd, 2023) Pandey, J.K.; Dinesh, M.H.; Kumar, G.N.IC engines, the backbone of the transportation sector is facing energy insecurity and stringent environmental norms motivating researchers to look for alternate ways of revival. In pursuit hydrogen and its careers are seen as promising option. Aiming the same comparative-study is performed on NH3/H2 (7:3) and hydrogen under varying ignition (from −24°CA to −12°CA) and EGR rates (till 25%). Results indicate improved combustion for NH3/H2 for a small range of ignition than hydrogen, ∂P/∂θ and ∂Q/∂θ is improved before TDC and deteriorates after it. Cycle-by-cycle variations increase for a longer ignition range for NH3/H2, but NOx drops more rapidly. At −24°CA, NH3/H2 has observed a minimal gap in peak pressure, CoV and performance from hydrogen. Though a small EGR helps reduce NOx, cycle-by-cycle variations and CA90 reduce due to improved combustion for NH3/H2. ∂P/∂θ and ∂Q/∂θ improve for the same range too. However, hydrogen suffers adverse effects due to EGR that intensify with increasing EGR-rate. At higher EGR, unstable combustion and heterogeneity prevail, resulting in increased cycle-by-cycle variations and a rapid drop in peak pressure. The prolonged combustion witnesses a massive decline in NOx for both fuels; however, the gap between NH3/H2 and hydrogen entities reduces. NH3/H2 shows better efficiency than hydrogen for an efficient NOx control. However, higher fuel NOx maintains a significant difference for NH3/H2 than hydrogen. The study limits quantitative analysis of it and also NH3 emissions, which is another primary concern. © 2023 Elsevier Ltd