scholarly journals Predictive Modeling of Surface Wear in Mechanical Contacts under Lubricated and Non-Lubricated Conditions

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1160
Author(s):  
Ali Rahman ◽  
Muhammad Khan ◽  
Aleem Mushtaq
Keyword(s):  
Predictive Modeling ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Good Accuracy ◽  
Spectral Features ◽  
Surface Wear ◽  
Average Wear ◽  
Airborne Noise ◽  
Pin On Disc ◽  
Mechanical Contacts

The surface wear in mechanical contacts under running conditions is always a challenge to quantify. However, the inevitable relationship between the airborne noise and the surface wear can be used to predict the latter with good accuracy. In this paper, a predictive model has been derived to quantify surface wear by using airborne noise signals collected at a microphone. The noise was generated from a pin on disc setup on different dry and lubricated conditions. The collected signals were analyzed, and spectral features estimated from the measurements and regression models implemented in order to achieve an average wear prediction accuracy of within 1mm3.

scholarly journals Machine Learning Techniques to Predict Soybean Plant Density Using UAV and Satellite-Based Remote Sensing

2021 ◽  
Vol 13 (13) ◽  
pp. 2548
Author(s):  
Luthfan Nur Habibi ◽  
Tomoya Watanabe ◽  
Tsutomu Matsui ◽  
Takashi S. T. Tanaka
Keyword(s):  
Spatial Variability ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Plant Density ◽  
Density Measurement ◽  
Critical Factor ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Soybean Plant ◽  
Climate Data

The plant density of soybean is a critical factor affecting plant canopy structure and yield. Predicting the spatial variability of plant density would be valuable for improving agronomic practices. The objective of this study was to develop a model for plant density measurement using several data sets with different spatial resolutions, including unmanned aerial vehicle (UAV) imagery, PlanetScope satellite imagery, and climate data. The model establishment process includes (1) performing the high-throughput measurement of actual plant density from UAV imagery with the You Only Look Once version 3 (YOLOv3) object detection algorithm, which was further treated as a response variable of the estimation models in the next step, and (2) developing regression models to estimate plant density in the extended areas using various combinations of predictors derived from PlanetScope imagery and climate data. Our results showed that the YOLOv3 model can accurately measure actual soybean plant density from UAV imagery data with a root mean square error (RMSE) value of 0.96 plants m−2. Furthermore, the two regression models, partial least squares and random forest (RF), successfully expanded the plant density prediction areas with RMSE values ranging from 1.78 to 3.67 plant m−2. Model improvement was conducted using the variable importance feature in RF, which improved prediction accuracy with an RMSE value of 1.72 plant m−2. These results demonstrated that the established model had an acceptable prediction accuracy for estimating plant density. Although the model could not often evaluate the within-field spatial variability of soybean plant density, the predicted values were sufficient for informing the field-specific status.

scholarly journals Application of Machine Learning Techniques to Predict Mechanical Properties for Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for production of end-use parts. Prediction of mechanical properties can be one of the possible ways to improve the repeatability of the results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress and elongation at break for polyamide 2200 (also known as PA12). EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models have prediction accuracy higher than 80% only for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about material properties, these models need to be improved in the future based on additional experimental work.

scholarly journals Risk Assessment Models for Wheat Fusarium Head Blight Epidemics Based on Within-Season Weather Data

2003 ◽  
Vol 93 (4) ◽  
pp. 428-435 ◽  
Author(s):  
E. D. De Wolf ◽  
L. V. Madden ◽  
P. E. Lipps
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Weather Conditions ◽  
Predictor Variables ◽  
Stepwise Logistic Regression ◽  
Head Blight ◽  
Logistic Regression Models

Logistic regression models for wheat Fusarium head blight were developed using information collected at 50 location-years, including four states, representing three different U.S. wheat-production regions. Non-parametric correlation analysis and stepwise logistic regression analysis identified combinations of temperature, relative humidity, and rainfall or durations of specified weather conditions, for 7 days prior to anthesis, and 10 days beginning at crop anthesis, as potential predictor variables. Prediction accuracy of developed logistic regression models ranged from 62 to 85%. Models suitable for application as a disease warning system were identified based on model prediction accuracy, sensitivity, specificity, and availability of weather variables at crop anthesis. Four of the identified models correctly classified 84% of the 50 location-years. A fifth model that used only pre-anthesis weather conditions correctly classified 70% of the location-years. The most useful predictor variables were the duration (h) of precipitation 7 days prior to anthesis, duration (h) that temperature was between 15 and 30°C 7 days prior to anthesis, and the duration (h) that temperature was between 15 and 30°C and relative humidity was greater than or equal to 90%. When model performance was evaluated with an independent validation set (n = 9), prediction accuracy was only 6% lower than the accuracy for the original data sets. These results indicate that narrow time periods around crop anthesis can be used to predict Fusarium head blight epidemics.

scholarly journals Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Vol 9 (6) ◽  
pp. 1060
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for the manufacturing industry due to flexibility in its design and functionality, but inconsistency in quality is one of the major limitations preventing utilizing this technology for the production of end-use parts. The prediction of mechanical properties can be one of the possible ways to improve the repeatability of results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress, and elongation at break for polyamide 2200 (also known as PA12). An EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models only have prediction accuracy higher than 80% for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about the material properties, these models need to be improved in the future based on additional experimental work.

scholarly journals Multiscale Latent Variable Regression

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Mohamed N. Nounou ◽  
Hazem N. Nounou
Keyword(s):  
Latent Variable ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Multiple Scales ◽  
Principal Component Regression ◽  
Principal Component ◽  
Measurement Noise ◽  
Process Data ◽  
Latent Variable Regression ◽  
Modeling Algorithm

Multiscale wavelet-based representation of data has been shown to be a powerful tool in feature extraction from practical process data. In this paper, this characteristic of multiscale representation is utilized to improve the prediction accuracy of some of the latent variable regression models, such as Principal Component Regression (PCR) and Partial Least Squares (PLS), by developing a multiscale latent variable regression (MSLVR) modeling algorithm. The idea is to decompose the input-output data at multiple scales using wavelet and scaling functions, construct multiple latent variable regression models at multiple scales using the scaled signal approximations of the data and then using cross-validation, and select among all MSLVR models the model which best describes the process. The main advantage of the MSLVR modeling algorithm is that it inherently accounts for the presence of measurement noise in the data by the application of the low-pass filters used in multiscale decomposition, which in turn improves the model robustness to measurement noise and enhances its prediction accuracy. The advantages of the developed MSLVR modeling algorithm are demonstrated using a simulated inferential model which predicts the distillate composition from measurements of some of the trays' temperatures.

scholarly journals Experimental assessment of multiple contact wear using airborne noise under dry and lubricated conditions

2017 ◽  
Vol 231 (12) ◽  
pp. 1503-1516 ◽  
Author(s):  
MA Khan ◽  
Kanza Basit ◽  
SZ Khan ◽  
KA Khan ◽  
AG Starr
Keyword(s):  
Wear Area ◽  
Contact Conditions ◽  
Noise Parameters ◽  
Recorded Sound ◽  
Airborne Noise ◽  
Pin On Disc ◽  
Noise Data ◽  
Wear Damage

The generation of wear and airborne noise is inevitable in the mechanical contacts of the machine components. This paper addresses the effectiveness of the airborne noise data in estimating the wear on a disc under multi-contact conditions. A pin-on-disc rig was employed to study the role of noise parameters on the evolution of the wear area. When a pin slides on the disc, the airborne noise is generated and subsequently a sound signal is obtained. These signals, for various sets of experiments, were recorded using a digital microphone. A Matlab code was developed and employed to estimate the noise parameters from the recorded sound. Noise parameters including values of voltage RMS, noise counts and amplitudes of dominant frequencies were used to analyse the variation in the disc wear at different time intervals. These parameters were found to be effective in the determination of the wear damage evaluation under different loads without lubrication.

scholarly journals Correlation between Spectral Characteristics and Physicochemical Parameters of Soda-Saline Soils in Different States

2019 ◽  
Vol 11 (4) ◽  
pp. 388 ◽  
Author(s):  
Xiaojie Li ◽  
Jianhua Ren ◽  
Kai Zhao ◽  
Zhengwei Liang
Keyword(s):  
Physicochemical Parameters ◽  
Regression Models ◽  
Spectral Characteristics ◽  
Electronic Conductivity ◽  
Salt Content ◽  
Surface States ◽  
Absolute Error ◽  
Saline Soils ◽  
Spectral Features ◽  
Linear Regression Models

The spectral features of soils are a comprehensive representation of their physicochemical parameters, surface states, and internal structures. To date, spectral measurements have been mostly performed for powdered soils and smooth aggregate soils, but rarely for cracked soils; a common state of soda-saline soils. In this study, we measured the spectral features of 57 saline soil samples in powdered, aggregate, and cracked states for comparison. We then explored in depth the factors governing soil spectral features to build up simple and multiple linear regression models between the spectral features and physicochemical parameters (salt content, Na+, pH, and electronic conductivity (EC)) of saline soils in different states. We randomly selected 40 samples to construct the models, and used the remaining 17 samples for validation. Our results indicated that the regression models worked more effectively in predicting physicochemical parameters for cracked soils than for other soils. Subsequently, the crack ratio (CR) was introduced into the regression models to modify the spectra of soils in powdered and aggregate states. The accuracy of prediction was improved, evidenced by a 2–11% decrease in the parameters mean absolute error (MAE).

Role of Regression Models in Bridge Pier Scour Prediction

2020 ◽  
Vol 11 (2) ◽  
pp. 156-170
Author(s):  
Roshni Thendiyath ◽  
Vijay Prakash
Keyword(s):  
Prediction Accuracy ◽  
Regression Models ◽  
Empirical Models ◽  
Bridge Pier ◽  
Pier Scour ◽  
Flow Parameters ◽  
Mars Model ◽  
Input Parameters ◽  
Hidden Neurons ◽  
Bridge Pier Scour

Scour monitoring is an important concern in the design of any hydraulic structure. This study introduces the application of regression models in the prediction of scour depth around a bridge pier. Feedforward Neural Network (FFNN) and Multivariate Adaptive Regression Spline (MARS) models have been developed using different flow parameters. The flow parameters taken into consideration are the flow depth, flow velocity, pier diameter, and Froude's number. The FFNN models with different combinations of input parameters along with a simultaneous variation in the number of hidden neurons were developed to further increase the prediction accuracy. The best combination of hidden neurons and input parameters was selected and compared with the developed MARS model. Further, these models were compared with the selected empirical models to find out the best possible model for bridge pier scour prediction. All the developed regression models and selected empirical models were compared using standard statistical performance evaluation measures such as Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Percentage BIAS (PBIAS). The FFNN model developed with 4-input parameters performed better compared with other combinations of input parameters. The performance indices of all developed models show that as the input parameter increases, prediction accuracy also increases. A superior prediction accuracy was observed with FFNN model with 4-input parameters compared to MARS model and other selected empirical models.

scholarly journals Identifying and Predicting Major Factors Affecting the Suicides in Sri Lanka

2019 ◽  
pp. 1-7
Author(s):  
S. M. M. Lakmali ◽  
L. S. Nawarathna
Keyword(s):  
Sri Lanka ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Negative Binomial ◽  
Information Criterion ◽  
Data Set ◽  
Factors Affecting ◽  
Married People ◽  
Civil Status ◽  
Major Factors

Aims: Identifying factors related to suicide and the prediction of future suicides are very important because suicide becomes a significant factor that engaged with education, social status, age, gender and many other factors. Therefore, the main objective of this study is to find the civil and education factors effecting on suicidal attempts in Sri Lanka and propose a model to predict the future suicides. Study Design: Statistical analysis with descriptive analysis and proposing models for predicting future suicides. Place and Duration of Study: Data collected from the Department of Police, Sri Lanka, between January 2006 and December 2016. Methodology: Data set has separated into two categories namely ‘civil data’ and ‘educational data’. We modeled the data from 2006 to 2011 and the data from 2014 to 2016 were used for model validation purposes. Quasi Poisson and negative binomial regression models were fitted to identify the major factors affecting suicide in both categories. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values were used to select the best model. Further, the Mean Absolute Percentage Deviation (MAPD) and Symmetric Mean Absolute Percent Error (SMAPE) were calculated to find the prediction accuracy of the proposed models. Results: For both regression models, the variables age, gender and level of education are significant for the models fitted for educational data, and civil status and gender are significant in the civil status dataset. According to the analysis, highest suicides were recorded for the age groups 21-30 and over 61 males, minimally-educated and married people. By considering the MAPD values, the prediction accuracy of both Quasi Poisson models and Negative binomial models were above 99%. But the negative binomial model is the best model because of the comparable high accuracy than the other model. A considerable reduction in suicides was obtained in 2010, due to the peaceful situation in Sri Lanka after the civil war. It is observed that by paying special attention to teenagers, old-aged and married people can reduce the number of suicides.

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