Field Reliability Estimation of Agricultural Tractors Based on Warranty Data

2021 ◽  
Vol 64 (2) ◽  
pp. 705-714
Author(s):  
Zhilin Zhao ◽  
Fang Cheng
Keyword(s):  
Regression Model ◽  
List Type ◽  
Reliability Parameters ◽  
Light Gradient ◽  
Manufacturing Company ◽  
Agricultural Tractors ◽  
Warranty Claims ◽  
Field Reliability ◽  
Usage Context ◽  
Usage Rates

HighlightsA LightGBM regression model for predicting tractor usage rates was established based on warranty data and considering agricultural tractors’ usage context (region and season) and was then interpreted using SHAP.The field reliability of tractors was estimated based on the usage of failed and unfailed tractors, after unfailed tractors’ usage was imputed using the LightGBM regression model.The proposed methodology was validated by predicting warranty claims using estimated reliability parameters.The proposed methodology was demonstrated using warranty data from a tractor manufacturing company in China.Abstract. Warranty data provide a valuable source of information for assessing the reliability of products in operation (called the field reliability). However, warranty data consist of failure information only. The unavailability of usage data for unfailed products makes it difficult to estimate the reliability of durable products such as agricultural tractors, for which usage is a greater concern than age for reliability analysis. Several studies have proposed methods to address this problem, but they did not include information on the usage context. This study proposes a methodology to estimate the field reliability of agricultural tractors from warranty data considering the tractors’ usage context. First, by taking features representing tractors’ usage context as the input, a usage rate regression model was established using a light gradient boosting machine (LightGBM). The usage of unfailed tractors was then generated. Finally, parametric estimates of the tractors’ reliability were determined based on the usage of failed and unfailed tractors. By interpreting the LightGBM model using SHapley Additive exPlanations (SHAP), it was found that tractors that were used more days in October and April had higher predicted usage rates. To validate the effectiveness of the proposed methodology, the estimated reliability parameters were used to predict the warranty claims of six types of tractors. The results showed that the proposed methodology performed the best in four cases and close to the best in two other cases when compared with two other baseline methods. The proposed methodology was demonstrated using warranty data from an agricultural tractor manufacturing company in China and can be applied to improve understanding of tractor reliability. Keywords: Field reliability, LightGBM, SHAP, Usage context, Warranty data.

Reduction of Torsional Vibration in Resonance Phenomena for Tractor Power Take-Off Drivelines Using Torsional Damper

2021 ◽  
Vol 64 (2) ◽  
pp. 365-376
Author(s):  
Da-Vin Ahn ◽  
In-Kyung Shin ◽  
Jooseon Oh ◽  
Woo-Jin Chung ◽  
Hyun-Woo Han ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Torsional Vibration ◽  
Simulation Software ◽  
Drive Shaft ◽  
Torsional Stiffness ◽  
List Type ◽  
Two Stage ◽  
Mass Moment ◽  
Simulation Results ◽  
Agricultural Tractors

HighlightsRattling of tractor power take-off drivelines can be detrimental to operators.A novel driveline model, which includes a torsional damper, was constructed.The behavior of the model was validated against that of an actual tractor driveline.The validated model was used to determine the optimal torsional damper parameters.These optimal parameters were validated by laboratory tests.Abstract. Rattle noise and high levels of vibration in agricultural tractors lower the productivity of the operators and may cause serious health issues in them. This study examined a method for preventing resonance and reducing the torsional vibration that causes rattling in tractor power take-off (PTO) drivelines in the idle state using a two-stage torsional damper. The PTO driveline was simplified to a 6-DOF model based on the principle of equivalent mass moment of inertia using commercial simulation software. The variations in the angular velocity of the PTO drive shaft in an actual tractor were measured and compared to the simulation results using a single-stage torsional damper to validate the model. Using this validated PTO driveline model, the pre spring of a two-stage torsional damper was investigated to determine its optimal torsional stiffness to minimize torsional vibration. The simulation results showed that the variations in the angular velocity of the PTO drive shaft decreased as the torsional stiffness of the pre spring decreased; accordingly, an appropriate torsional stiffness reduced the variation in the angular velocity delivered to the PTO drive shaft. The optimal torsional stiffness of the pre spring was determined by considering the manufacturing limitations of the torsional damper and the magnitude of the input engine torque. A pre spring with this optimal torsional stiffness was installed on an actual PTO driveline to measure the angular velocity transmissibility, which was the ratio of the variation in the angular velocity of the engine flywheel to the variation in the angular velocity of the PTO drive shaft, and the results were compared with those of the simulation. When the angular velocity of the engine was 850 rpm, the angular velocity transmissibility of the PTO drive shaft was 0.4 in the actual test, similar to the value of 0.29 obtained using the simulation. Thus, the simulation-optimized pre spring was able to avoid the resonance domain, while considerably reducing the torsional vibration that leads to rattling. The results of this study support the safe operation of agricultural tractors and guide the evaluation of torsional damper configurations of different vehicles. Keywords: PTO driveline, Resonance, Simulation model, Torsional damper, Torsional vibration, Tractor rattle.

scholarly journals Prediction Modeling Studies for Medical Usage Rates in Mass Gatherings: A Systematic Review

2019 ◽  
Vol 34 (s1) ◽  
pp. s40-s40
Author(s):  
Hans Van Remoortel ◽  
Hans Scheers ◽  
Emmy De Buck ◽  
Karen Lauwers ◽  
Philippe Vandekerckhove
Keyword(s):  
Systematic Review ◽  
Regression Model ◽  
Free Water ◽  
Dew Point ◽  
Statistical Regression ◽  
Prediction Modeling ◽  
Mass Gatherings ◽  
Modeling Studies ◽  
Usage Rates

Introduction:Mass gatherings attended by large crowds are an increasingly common feature of society. In parallel, an increased number of studies have been conducted to identify those variables that are associated with increased medical usage rates.Aim:To identify studies that developed and/or validated a statistical regression model predicting patient presentation rate (PPR) or transfer to hospital rate (TTHR) at mass gatherings.Methods:Prediction modeling studies from 6 databases were retained following systematic searching. Predictors for PPR and/or TTHR that were included in a multivariate regression model were selected for analysis. The GRADE methodology (Grades of Recommendation, Assessment, Development, and Evaluation) was used to assess the quality of evidence.Results:We identified 11 prediction modeling studies with a combined audience of >32 million people in >1500 mass gatherings. Eight cross-sectional studies developed a prediction model in a mixed audience of (spectator) sports events, music concerts, and public exhibitions. Statistically significant variables (p<0.05) to predict PPR and/or TTHR were as follows: accommodation (seated, boundaries, indoor/outdoor, maximum capacity, venue access), type of event, weather conditions (humidity, dew point, heat index), crowd size, day vs night, demographic variables (age/gender), sports event distance, level of competition, free water availability, and specific TTHR-predictive factors (injury status: number of patient presentations, type of injury). The quality of the evidence was considered as low. Three studies externally validated their model against existing models. Two validation studies showed a large underestimation of the predicted patients presentations or transports to hospital (67-81%) whereas one study overestimated these outcomes by 10-28%.Discussion:This systematic review identified a comprehensive list of relevant predictors which should be measured to develop and validate future models to predict medical usage at mass gatherings. This will further scientifically underpin more effective pre-event planning and resource provision.

Power-Saving Solutions for Pre-Compensated Load-Sensing Systems on Mobile Machines

2021 ◽  
Vol 64 (5) ◽  
pp. 1435-1448
Author(s):  
Xin Tian ◽  
Patrick Stump ◽  
Andrea Vacca ◽  
Stefano Fiorati ◽  
Francesco Pintore
Keyword(s):  
Energy Efficiency ◽  
Hydraulic System ◽  
Power Saving ◽  
Control Valve ◽  
Cost Effective ◽  
Fluid Power ◽  
Flow Control Valve ◽  
List Type ◽  
Load Sensing ◽  
Agricultural Tractors

HighlightsTwo methods (VPM and HVM) are proposed to improve the hydraulic system efficiency of agricultural tractors.VPM and HVM both target reducing the power loss at the flow control valve of the hydraulic system.The solutions are presented conceptually and then numerically modeled, and VPM is tested on an actual tractor.Results show that the VPM solution achieves 6.7% power saving, while HVM achieves 15.6% power saving.Abstract. Load sensing (LS) is a dominant fluid power actuation technology in mobile machines, particularly in construction and agriculture. It has the advantage of guaranteeing good controllability in systems with multiple actuators while promoting higher energy efficiency. Several variants of LS systems have been proposed over the years, and research on cost-effective methods to further increase their efficiency is still of interest for original equipment manufacturers (OEMs) and the fluid power community. This article presents two solution, referred to as variable pump margin (VPM) and hybrid variable margin (HVM), suitable to improve the energy efficiency in pre-compensated LS systems such as those used in agricultural tractors. Both methods allow either downsizing the control valves or reducing the power consumption over the working range. Compared to a standard LS system, the VPM solution lowers the pump pressure using an electronic proportional pressure-reducing valve (ep-PRV), while the HVM solution uses a second ep-PRV in the compensator’s pilot line to further minimize the pressure differential across the LS valve. Simulation and experimental results show that, among the main working conditions, the VPM solution on average achieved 6.7% power saving over the standard LS system, while the model predicted an average improvement of 15.6% for the HVM solution. Keywords: Efficiency, Experiments, Hydraulic, Load sensing, Modeling, Pump.

scholarly journals To the Issue of Determining Agricultural Tractor Slipping

2021 ◽  
pp. 14-18
Author(s):  
A.B. Ivanov ◽  
◽  
V.E. Tarkivsky ◽  
V.Yu. Revenko ◽  
◽  
...  
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Polynomial Regression ◽  
Local Polynomial Regression ◽  
Local Polynomial ◽  
Agricultural Tractors ◽  
Engine Crankshaft ◽  
Agricultural Tractor

The well-known methods for determining the slipping of the propulsion devices of agricultural tractors are described. A regression analysis of the traction characteristics of a wheeled tractor is performed. The equivalence of the method for determining the current slippage through the actual tractor speed and engine crankshaft speed is assessed. A regression model is proposed to determine the amount of slippage based on the method of local polynomial regression (locally estimated scatterplot smoothing or LOESS)

scholarly journals Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Christopher Sutton ◽  
Luca M. Ghiringhelli ◽  
Takenori Yamamoto ◽  
Yury Lysogorskiy ◽  
Lars Blumenthal ◽  
...  
Keyword(s):  
Regression Model ◽  
Materials Science ◽  
Pearson Correlation ◽  
Graph Representation ◽  
Gradient Boosting ◽  
Bandgap Energy ◽  
Prediction Errors ◽  
Crowd Sourcing ◽  
Light Gradient ◽  
Public Data

AbstractA public data-analytics competition was organized by the Novel Materials Discovery (NOMAD) Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000 (AlxGayIn1–x–y)2O3 compounds. Its aim was to identify the best machine-learning (ML) model for the prediction of two key physical properties that are relevant for optoelectronic applications: the electronic bandgap energy and the crystalline formation energy. Here, we present a summary of the top-three ranked ML approaches. The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials. The second-place model combined many candidate descriptors from a set of compositional, atomic-environment-based, and average structural properties with the light gradient-boosting machine regression model. The third-place model employed the smooth overlap of atomic position representation with a neural network. The Pearson correlation among the prediction errors of nine ML models (obtained by combining the top-three ranked representations with all three employed regression models) was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance. Ensembling relatively decorrelated models (based on the Pearson correlation) leads to an even higher prediction accuracy.

Development of Prediction Model for Axle Torque of Agricultural Tractors

2020 ◽  
Vol 63 (6) ◽  
pp. 1773-1786 ◽  
Author(s):  
Wan-Soo Kim ◽  
Yeon-Soo Kim ◽  
Yong-Joo Kim
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Traction Force ◽  
Field Tests ◽  
Travel Speed ◽  
List Type ◽  
Slip Ratio ◽  
Agricultural Tractors ◽  
Torque Values ◽  
Agricultural Tractor

HighlightsA prediction model was developed for estimating the axle torque of an agricultural tractor.The model was developed by complementing and modifying a previously proposed traction equation.Compared to the actual axle torque, the proposed model attained MAPE of 2.1%, RMSE of 29 Nm, and RD of 2.7%.The model predicted axle torque more accurately than the traction force-based prediction model.Abstract. The tractor driving axle torque is an important factor in optimal transmission design and service life evaluation. Axle torque measurement sensor systems are very expensive, and traction force-based axle torque prediction models cannot accurately estimate the axle torque because they do not consider both the conditions of the tractor and the attached implement. Therefore, in this study, a prediction model was developed to estimate the axle torque of an agricultural tractor based on the traction force equation and motion resistance. A load measurement system was established to verify the developed prediction model, and actual field torque data were collected through field tests. The developed prediction model was verified by comparing the results of five reference prediction methods, including weight, engine-rated torque, and three traction equations (Wismer-Luth, ASABE Standard D497.4, and Brixius), using the measured axle torque. Performance evaluation was conducted based on the main variables, including travel speed, tillage depth, and slip ratio. The proposed prediction model was found to be closest to the 1:1 line at all travel speeds, tillage depths, and slip ratios, implying that it can best explain the measured torque values among all prediction models. Compared to the other prediction models, the proposed prediction model’s results under all variable conditions had an R2 of 0.65, MAPE of 2.1%, RMSE of 29 Nm, and RD of 2.7%, indicating excellent prediction of the measured torque. The results show that the developed prediction model can be applied to axle torque prediction by explaining the actual measured axle torque. Keywords: Agricultural tractor, Axle torque, Prediction model, Torque estimation, Traction force.

scholarly journals PENGARUH INTEREST RATE, INVESTOR SENTIMENT, FINANCIAL DISTRESS TERHADAP STOCK RETURN

2020 ◽  
Vol 7 (1) ◽  
pp. 80-106
Author(s):  
Henryanto Wijaya
Keyword(s):  
Interest Rate ◽  
Regression Model ◽  
Financial Distress ◽  
Stock Return ◽  
Sampling Method ◽  
Stock Exchange ◽  
Investor Sentiment ◽  
Financial Statement ◽  
Analysis Tool ◽  
Manufacturing Company

The purpose of this research is to know the effect of interest rate, investor sentiment, and financial distress of stock return on manufacturing company listed on the Indonesia Stock Exchange from 2014-2017. The sampling method used in this research used 49 manufacturing company that were selected using purposive sampling method. Data used for this study is obtained from financial statement for the year ended December 31st during 2014-2017. Analysis tool that will be used to analyze the hypothesis with multiple linier regression model is software IBM SPSS (Statistical Product and Service Solutions) version 23.0 for Windows. The result for this research showed that interest rate and investor sentiment have positive and significant effect on stock return, while financial distress has negative and significant effect on stock return.

scholarly journals Coronametrics: The UK turns the corner

2020 ◽  
Author(s):  
Adam Goliński ◽  
Peter Spencer
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Regression Models ◽  
List Type ◽  
Short Term ◽  
Time Series Regression ◽  
Current Trends ◽  
Short Term Forecasting ◽  
The Uk ◽  
The Impact

Abstract*There are many ways of analyzing the progress of an epidemic, but when it comes to short term forecasting, it is very hard to beat a simple time series regression model. These are good at allowing for the noise in day to day observations, extracting the trend and projecting it forward.*Our regression models are designed to exploit this, using the daily statistics released by PHE and NHSE. These strongly suggest that the tide has turned and that taking one day with the next, the national figures for deaths from this virus will now fall back noticeably, easing the pressure on the NHS and its staff.*There is still a huge range of uncertainty associated with any forecast. The model is currently predicting a total of 113,000 admissions to UK hospitals by the end of April and that 19,000 people will die from the virus in English hospitals by then. There is a 1 in 20 chance that the mortality figures could flatten out more quickly, with around 1,000 more deaths occurring by the end of April. However, there is the same risk that this figure continues to mount, rising to a total of 24,000 by the end of the month. On current trends, the number of deaths in the UK is likely to be 10% higher than the number in England.*Longer term, the impact of the virus will depend critically upon the likely relaxation of the current government strategy of suppression.

scholarly journals Pengaruh corporate governance, ukuran perusahaan dan leverage terhadap corporate social responsibility (Studi empiris pada perusahaan manufaktur yang terdaftar tahun 2014-2017 di Bursa Efek Indonesia)

2020 ◽  
Vol 2 (2) ◽  
pp. 173
Author(s):  
Fitriyah Fitriyah
Keyword(s):  
Corporate Governance ◽  
Corporate Social Responsibility ◽  
Panel Data ◽  
Social Responsibility ◽  
Regression Model ◽  
Firm Size ◽  
Independent Variables ◽  
Manufacturing Company ◽  
Corporate Social

This study aims to examine the effect of corporate governance, firm size, and leverage on corporate social responsibility on Indonesian listed manufacturing company during period 2014 to 2017. The independent variables of this study are corporate governance, firm size and leverage, while the dependent variable is corporate social responsibility. This study uses panel data to analyze the regression model with assistance of EViews 8. The results of this study indicate that: (1) Board of Commisioners insignificantly effect on corporate social responsibility; (2) the Board of Commissioners independent significant effect on corporate social responsibility; (3) the Audit Committe insignificantly effect on corporate social responsibility; (4) firm size insignificantly effect on corporate social responsibility; and (5) leverage significant effect on corporate social responsibility.

scholarly journals Hepatic resection versus transarterial chemoembolization for the intermediate-stage hepatocellular carcinoma : a predicted mortality risk-based decision analysis

2020 ◽  
Author(s):  
Linbin Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Rate ◽  
Regression Model ◽  
Hepatic Resection ◽  
Selection Criterion ◽  
Group Versus ◽  
Intermediate Stage ◽  
List Type ◽  
Transarterial Chemoembolisation ◽  
Tace Group

AbstractBackgroundThe selection criterion for hepatic resection(HR) in intermediate-stage(IM) hepatocellular carcinoma(HCC) is still controversial. This study aims to compare transarterial chemoembolisation (TACE) and HR in the range of predicted overall mortality(OM).MethodsIn all, 946 consecutive patients with IM-HCC were categorised in HR and TACE group. We performed multivariable Cox regression model to predict OM in HR patients. To evaluate the HR impact on OM concerning baseline characteristics, we test the interaction between predicted OM risk and HR status. The cut-off values were determined by two-piece-wise linear regression model and decision curve analysis. Also, the inverse probability of treatment weight was performed to minimise potential bias as a sensitivity analysis.FindingsTotally, 23.0% (n=225) of patients received HR. The 5-yr overall survival rate was higher in the HR group versus the TACE group (52.3% vs 22.8%; p<0.0001). In the HR group, five predictors (all<0.05) were selected to calculate the 5-yr OM risk. This model also used to predict the 5-yr OM-free survival rate. The line of HR and TACE was crossing with predicted OM risk at 100%. The benefit of HR versus TACE decreased progressively as predicted OM risk>55%. When OM risk >80%, HR was not significantly superior to TACE (HR:0.61;95%CI:0.31,1.21), and both HR and TACE did not increase net benefit.InterpretationHepatic resection was superior to transarterial chemoembolisation for intermediate-stage hepatocellular carcinoma at the 5-yr OM risk<80%. And TACE was suitable for the patients with OM risk>80%.Fundingnone.SynopsisThe line of HR and TACE was crossing with predicted OM risk at 100%The benefit of HR versus TACE decreased progressively as predicted OM risk>55%When OM risk >80%, HR was not significantly superior to TACE

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