scholarly journals Engine Vibration Data Increases Prognosis Accuracy on Emission Loads: A Novel Statistical Regressions Algorithm Approach for Vibration Analysis in Time Domain

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1234
Author(s):  
Tadas Žvirblis ◽  
Darius Vainorius ◽  
Jonas Matijošius ◽  
Kristina Kilikevičienė ◽  
Alfredas Rimkus ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Mass Flow ◽  
Time Domain ◽  
Diesel Engines ◽  
Prediction Accuracy ◽  
Exhaust Emission ◽  
Statistical Regression ◽  
Analysis Algorithm ◽  
Ancova Model ◽  
Engine Power

Statistical regression models have rarely been used for engine exhaust emission parameters. This paper presents a three-step statistical analysis algorithm, which shows increased prediction accuracy when using vibration and sound pressure data as a covariate variable in the exhaust emission prediction model. The first step evaluates the best time domain statistic and the point of collection of engine data. The univariate linear regression model revealed that non-negative time domain statistics are the best predictors. Also, only one statistic evaluated in this study was a statistically significant predictor for all 11 exhaust parameters. The ecological and energy parameters of the engine were analyzed by statistical analysis. The symmetry of the methods was applied in the analysis both in terms of fuel type and in terms of adjustable engine parameters. A three-step statistical analysis algorithm with symmetric statistical regression analysis was used. Fixed engine parameters were evaluated in the second algorithm step. ANOVA revealed that engine power was a strong predictor for fuel mass flow, CO, CO2, NOx, THC, COSick, O2, air mass flow, texhaust, whereas type of fuel was only a predictor of tair and tfuel. Injection timing did not allow predicting any exhaust parameters. In the third step, the best fixed engine parameter and the best time domain statistic was used as a model covariate in ANCOVA model. ANCOVA model showed increased prediction accuracy in all 11 exhausted emission parameters. Moreover, vibration covariate was found to increase model accuracy under higher engine power (12 kW and 20 kW) and using several types of fuels (HVO30, HVO50, SME30, and SME50). Vibration characteristics of diesel engines running on alternative fuels show reliable relationships with engine performance characteristics, including amounts and characteristics of exhaust emissions. Thus, the results received can be used to develop a reliable and inexpensive method to evaluate the impact of various alternative fuel blends on important parameters of diesel engines.

Effect of H2 and CO Content in Syngas on the Performance and Emission of Syngas-Diesel Dual Fuel Engine - A Review

2014 ◽  
Vol 699 ◽  
pp. 648-653 ◽  
Author(s):  
Bahaaddein K.M. Mahgoub ◽  
Suhaimi Hassan ◽  
Shaharin Anwar Sulaiman
Keyword(s):  
Carbon Monoxide ◽  
Diesel Engines ◽  
Combustion Process ◽  
Exhaust Emission ◽  
Dual Fuel ◽  
Pure Liquid ◽  
Compression Ignition ◽  
Performance And Emission ◽  
Combustion Duration ◽  
Combustible Gases

In this review, a series of research papers on the effects of hydrogen and carbon monoxide content in syngas composition on the performance and exhaust emission of compression ignition diesel engines, were compiled. Generally, the use of syngas in compression ignition (CI) diesel engine leads to reduce power output due to lower heating value when compared to pure liquid diesel mode. Therefore, variation in syngas composition, especially hydrogen and carbon monoxide (Combustible gases), is suggested to know the appropriate syngas composition. Furthermore, the simulated model of syngas will help to further explore the detailed effects of engine parameters on the combustion process including the ignition delay, combustion duration, heat release rate and combustion phasing. This will also contribute towards the efforts of improvement in performance and reduction in pollutants’ emissions from CI diesel engines running on syngas at dual fuel mode. Generally, the database of syngas composition is not fully developed and there is still room to find the optimum H2 and CO ratio for performance, emission and diesel displacement of CI diesel engines.

THE CHANCES FOR REDUCTION OF VIBRATIONS IN MECHANICAL SYSTEM WITH LOW-EMISSION SHIPS COMBUSTION ENGINES

2021 ◽  
Vol 157 (A4) ◽  
Author(s):  
R Grega ◽  
J Homišin ◽  
M Puškár ◽  
J Kul’ka ◽  
J Petróci ◽  
...  
Keyword(s):  
Mechanical System ◽  
Diesel Engines ◽  
Fuel Mixture ◽  
Adverse Impact ◽  
Exhaust Gas ◽  
Combustion Engines ◽  
Low Emission ◽  
Power Outputs ◽  
Exhaust Gas Emissions ◽  
Engine Power

Development of diesel engines is focused on reduction of exhaust gas emissions, increase of efficiency of the fuel mixture combustion and decrease of fuel consumption. Such engines are referred to as low-emission engines. Low- engines trends bring higher engine power outputs, torques and also increase of vibrations and noisiness level. In order to reduce these vibrations of diesel engines, it is necessary to apply different dynamical elements, which are able to increase an adverse impact of exciting amplitudes. One of the results is application of a pneumatic dual-mass flywheel. The pneumatic dual-mass flywheel is a dynamical element that consists of two masses (the primary and the secondary mass), which are jointed together by means of a flexible interconnection. This kind of the flywheel solution enables to change resonance areas of the mechanical system which consequently leads to reduction of vibrations.

scholarly journals NUMERICAL COMPARISON OF WAVE SYNTHESIS METHODS

1988 ◽  
Vol 1 (21) ◽  
pp. 6
Author(s):  
M.D. Miles ◽  
E.R. Funke
Keyword(s):  
Statistical Analysis ◽  
Time Domain ◽  
Synthesis Methods ◽  
Numerical Comparison ◽  
Comparison Study ◽  
Order Group ◽  
Wave Parameters ◽  
Long Wave ◽  
Standard Wave ◽  
Wave Synthesis

A numerical comparison study is carried out on a variety of methods for synthesizing pseudo-random Gaussian wave records for laboratory wave generation. Three nonharmonic superposition methods and three time domain filtering procedures are compared to a harmonic FFT technique. The synthesis methods are evaluated on the basis of a statistical analysis of 16 standard wave parameters obtained from a set of 200 wave records. Second order group-bounded long wave components are also investigated.

scholarly journals Using Different Assessment Indicators in Supporting Online Learning

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Online Learning ◽  
Statistical Analysis ◽  
Learning Outcomes ◽  
Learning Process ◽  
Learning Outcome ◽  
Learning System ◽  
Difficulty Level ◽  
Learning Methods ◽  
Analysis Algorithm ◽  
Efficient Learning

The assessment outcome for many online learning methods are based on the number of correct answers and than convert it into one final mark or grade. We discovered that when using online learning, we can extract more detail information from the learning process and these information are useful for the assessor to plan an effective and efficient learning model for the learner. Statistical analysis is an important part of an assessment when performing the online learning outcome. The assessment indicators include the difficulty level of the question, time spend in answering and the variation in choosing answer. In this paper we will present the findings of these assessment indicators and how it can improve the way the learner being assessed when using online learning system. We developed a statistical analysis algorithm which can assess the online learning outcomes more effectively using quantifiable measurements. A number of examples of using this statistical analysis algorithm are presented.

scholarly journals Hybrid Neuro-Fractal Analysis of ECG Signals to Predict Ischemia

2021 ◽  
Author(s):  
Hedieh Montazeri
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Statistical Analysis ◽  
Linear Regression ◽  
Fractal Analysis ◽  
Prediction Accuracy ◽  
Clinical Characteristics ◽  
Hybrid Approach ◽  
Binary Logistic Regression ◽  
Ecg Signals

In this thesis, we propose and implement a new hybrid approach using fractal analysis, statistical analysis and neural network computation to build a model for prediction the number of ischemia occurrence based on ECG recordings. The main advantage of the proposed approach over similar earlier related works is that first useful parameters from fractal analysis of the signal are extracted to build a model that includes both clinical characteristics and signal attributes. Statistical analysis such as binary logistic regression and multivariate linear regression are then used to further explore the relation of parameters in order to obtain a more accurate model. We show that the results compare well with those of earlier work and clearly indicate that the augmentation of the above mentioned approaches improves the prediction accuracy.

scholarly journals A Study on Characteristic Emission Factors of Exhaust Gas from Diesel Locomotives

2020 ◽  
Vol 17 (11) ◽  
pp. 3788 ◽  
Author(s):  
Min-Kyeong Kim ◽  
Duckshin Park ◽  
Minjeong Kim ◽  
Jaeseok Heo ◽  
Sechan Park ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Environmental Protection Agency ◽  
High Speed ◽  
Emission Factors ◽  
Exhaust Emission ◽  
Emission Measurement ◽  
Exhaust Gas ◽  
Diesel Locomotive ◽  
Diesel Vehicles ◽  
Engine Power

Use of diesel locomotives in transport is gradually decreasing due to electrification and the introduction of high-speed electric rail. However, in Korea, up to 30% of the transportation of passengers and cargo still uses diesel locomotives and diesel vehicles. Many studies have shown that exhaust gas from diesel locomotives poses a threat to human health. This study examined the characteristics of particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), and hydrocarbons in diesel locomotive engine exhaust. Emission concentrations were evaluated and compared with the existing regulations. In the case of PM and NOx, emission concentrations increased as engine output increased. High concentrations of CO were detected at engine start and acceleration, while hydrocarbons showed weakly increased concentrations regardless of engine power. Based on fuel consumption and engine power, the emission patterns of PM and gaseous substances observed in this study were slightly higher than the U.S. Environmental Protection Agency Tier standard and the Korean emission standard. Continuous monitoring and management of emissions from diesel locomotives are required to comply with emission standards. The findings of this study revealed that emission factors varied based on fuel consumption, engine power, and actual driving patterns. For the first time, a portable emission measurement system (PEMS), normally used to measure exhaust gas from diesel vehicles, was used to measure exhaust gas from diesel locomotives, and the data acquired were compared with previous results. This study is meaningful as the first example of measuring the exhaust gas concentration by connecting a PEMS to a diesel locomotive, and in the future, a study to measure driving characteristics and exhaust gas using a PEMS should be conducted.

scholarly journals Comparative Analysis on the Deployment of Machine Learning Algorithms in the Distributed Brillouin Optical Time Domain Analysis (BOTDA) Fiber Sensor

Photonics ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 79 ◽  
Author(s):  
Nur Dalilla Nordin ◽  
Mohd Saiful Dzulkefly Zan ◽  
Fairuz Abdullah
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Comparative Analysis ◽  
Time Domain ◽  
Prediction Accuracy ◽  
Processing Time ◽  
Time Domain Analysis ◽  
Fiber Sensor ◽  
Temperature Prediction ◽  
Optical Time

This paper demonstrates a comparative analysis of five machine learning (ML) algorithms for improving the signal processing time and temperature prediction accuracy in Brillouin optical time domain analysis (BOTDA) fiber sensor. The algorithms analyzed were generalized linear model (GLM), deep learning (DL), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). In this proof-of-concept experiment, the performance of each algorithm was investigated by pairing Brillouin gain spectrum (BGS) with its corresponding temperature reading in the training dataset. It was found that all of the ML algorithms have significantly reduced the signal processing time to be between 3.5 and 655 times faster than the conventional Lorentzian curve fitting (LCF) method. Furthermore, the temperature prediction accuracy and temperature measurement precision made by some algorithms were comparable, and some were even better than the conventional LCF method. The results obtained from the experiments would provide some general idea in deploying ML algorithm for characterizing the Brillouin-based fiber sensor signals.

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