An Unconventional Method for the Diagnosis and Study of Generator Rotor Thermal Bows

2021 ◽  
Author(s):  
Steven Chatterton ◽  
Paolo Pennacchi ◽  
Andrea Vania
Keyword(s):  
Process Parameters ◽  
Regression Models ◽  
Thermal Sensitivity ◽  
Diagnostic Technique ◽  
Diagnostic Techniques ◽  
Linear Regression Models ◽  
Friction Forces ◽  
Linear Relationships ◽  
Winding Insulation ◽  
Unconventional Method

Abstract The rotor thermal sensitivity often affects the dynamic behavior of power unit generators. Owing to this phenomenon, increments of field current and other process parameters that are related to it may cause a shaft thermal bow and significant changes in the synchronous vibration. This symptom can also be caused by many other common malfunctions that affect rotating machines. Therefore, diagnostic techniques aimed at identifying the actual fault are very useful for optimizing maintenance activities. The thermal sensitivity of generator rotors can be deemed as a fault because it is commonly caused by a local deterioration of the winding insulation as well as by jamming phenomena between conductors and rotor slots, caused by friction forces due to the different thermal expansions of these components. This paper shows the results obtained applying a diagnostic method, based on multiple linear regression models, which has been developed for the analysis of generator vibrations caused by thermal sensitivity. Nevertheless, non-linear relationships between vibration and process parameters have also been taken into account. The capabilities of this diagnostic technique have been validated using the analysis of experimental data collected in a power plant. The results of this investigation are shown and discussed in the paper.

An Unconventional Method for the Diagnosis and Study of Generator Rotor Thermal Bows

2021 ◽  
Author(s):  
Steven Chatterton ◽  
Paolo Pennacchi ◽  
Andrea Vania
Keyword(s):  
Process Parameters ◽  
Regression Models ◽  
Thermal Sensitivity ◽  
Diagnostic Technique ◽  
Diagnostic Techniques ◽  
Linear Regression Models ◽  
Friction Forces ◽  
Winding Insulation ◽  
Multiple Linear Regression Models ◽  
Unconventional Method

Abstract The rotor thermal sensitivity often affects the dynamic behavior of power unit generators. Owing to this phenomenon, increments of field current and other process parameters that are related to it may cause a shaft thermal bow and significant changes in the synchronous vibration. This symptom can also be caused by many other common malfunctions that affect rotating machines. Therefore, diagnostic techniques aimed at identifying the actual fault are very useful for optimizing maintenance activities. The thermal sensitivity of generator rotors can be deemed as a fault because it is commonly caused by a local deterioration of the winding insulation as well as by jamming phenomena between conductors and rotor slots, caused by friction forces due to the different thermal expansions of these components. This paper shows the results obtained applying a diagnostic method, based on multiple linear regression models, which has been developed for the analysis of generator vibrations caused by thermal sensitivity. Nevertheless, nonlinear relationships between vibration and process parameters have also been taken into account. The capabilities of this diagnostic technique have been validated using the analysis of experimental data collected in a power plant. The results of this investigation are shown and discussed in the paper.

scholarly journals Individual and joint association of bioavailable testosterone and aging with neutrophil-to-lymphocyte ratio in Chinese middle-aged and elderly men

2019 ◽  
Vol 32 (8) ◽  
pp. 1515-1523 ◽  
Author(s):  
Jian Zhou ◽  
Yaping Wei ◽  
Yuan Lan ◽  
Jingjing Zuo ◽  
Xiangqing Hou ◽  
...  
Keyword(s):  
Regression Models ◽  
Cross Sectional Study ◽  
Neutrophil To Lymphocyte Ratio ◽  
Linear Regression Models ◽  
Elderly Men ◽  
Cross Sectional ◽  
Lymphocyte Ratio ◽  
Bioavailable Testosterone ◽  
Linear Relationships ◽  
The Individual

Abstract Background and objectives Accumulating evidences suggest that chronic systemic inflammation (CSI) is independently associated with large number of major non-communicable chronic diseases (NCDs) ranging from metabolic disorders to cancers, and neutrophil-to-lymphocyte ratio (NLR) has been accepted as a novel, convenient marker for CSI response. Testosterone deficiency in men is linked to high risk of NCDs. This cross-sectional study aimed to investigate the individual and joint association of bioavailable testosterone (BIOT) and aging with NLR. Methods A total of 132 male adults were enrolled during Jan. 2011 and Oct. 2017 in the first affiliated hospital of University of Science and Technology of China. Local weighted regression (LOESS) and multivariable generalized linear regression models were utilized to comprehensively examine the individual and joint association between BIOT and age with NLR. Results Obvious linear relationships between NLR and BIOT or age were observed with the LOESS models. NLR was negatively correlated to BIOT after adjusting for some potential confounding factors (P = 0.034). As compared to the lowest quartile of BIOT, the adjusted decrease of NLR for the 2nd, 3rd and 4th quartiles were 0.40, 0.64 and 0.72, respectively. Meanwhile, NLR was observed to be independently correlated to elevated age (P = 0.043). Furthermore, as compared to the counterparts, men over 70 years combined with plasma BIOT less than 4.7 nmol/L had the highest NLR level, which suggested that low BIOT and aging jointly correlated to the level of NLR (P = 0.005). Conclusion BIOT deficiency and aging were individually and jointly correlated to CSI. Men over 70 years combined with BIOT < 4.7 nmol/L were more like to have higher grade of CSI than others.

scholarly journals Prediction of leaf number by linear regression models in cassava

1970 ◽  
Vol 9 (1) ◽  
pp. 49-54 ◽  
Author(s):  
MSA Fakir ◽  
MG Mostafa ◽  
MR Karim ◽  
AKMA Prodhan
Keyword(s):  
Linear Regression ◽  
Manihot Esculenta ◽  
Regression Models ◽  
Primary Branch ◽  
Leaf Abscission ◽  
Leaf Number ◽  
Linear Regression Models ◽  
Leaf Production ◽  
Linear Relationships ◽  
High Predictability

Estimation of leaf number currently held on the plant and degree of leaf sheding occurred was carried out in two Cassava (Manihot esculenta) morphotypes (Philippine and Nagra) at Mymensingh (24°75´N 90°50´E). Four linear regression Models were developed for estimating leaf number (LN) from length (L) of mainstem (MS) and primary branch (PB) and they were LNMS = -6.89 + 1.05LMS (Model # 1) and LNPB = -5.116 + 1.033LPB (Model # 2) for Philippine; and LNMS = -4.041 + 0.73LMS (Model # 3) and LNPB = -1.597 + 0.707LPB (Model # 4) for Nagra morphotype. New leaf number produced in the mainstem (LNMS) and primary branch (LNPB), total leaf number in the mainstem (TLMS) and primary branch (TLPB) of each morphotype were also counted for leaf abscission (LAB) prediction model and the results showed that the regression models of leaf abscission in the primary branch (LABPB) from new leaf in the primary branch (LNPB) was effective (LABPB = - 0.521 + 0.525LNPB) (Model # 6). These regression Models showed linear relationships when actual leaf number was plotted against predicted leaf number and that this confirmed accuracy of the developed Models. Moreover, Models selection indices had high predictability (high R2) with minimum error (low error mean square error and percentage deviation). The selected Models appeared accurate and rapid, but can be used for estimation of leaf production in Philippine and Nagra morphotypes of Cassava. Keywords: Manihot esculenta; Regression; Leaf production; Leaf abscission DOI: http://dx.doi.org/10.3329/jbau.v9i1.8743 JBAU 2011; 9(1): 49-54

scholarly journals Optimization of Process Parameters for Turning Hastelloy X under Different Machining Environments Using Evolutionary Algorithms: A Comparative Study

2021 ◽  
Vol 11 (20) ◽  
pp. 9725
Author(s):  
Vinothkumar Sivalingam ◽  
Jie Sun ◽  
Siva Kumar Mahalingam ◽  
Lenin Nagarajan ◽  
Yuvaraj Natarajan ◽  
...  
Keyword(s):  
Process Parameters ◽  
Regression Models ◽  
Research Work ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Cryogenic Machining ◽  
Linear Regression Models ◽  
Hastelloy X ◽  
Optimization Of Process Parameters ◽  
Optimal Set

In this research work, the machinability of turning Hastelloy X with a PVD Ti-Al-N coated insert tool in dry, wet, and cryogenic machining environments is investigated. The machinability indices namely cutting force (CF), surface roughness (SR), and cutting temperature (CT) are studied for the different set of input process parameters such as cutting speed, feed rate, and machining environment, through the experiments conducted as per L27 orthogonal array. Minitab 17 is used to create quadratic Multiple Linear Regression Models (MLRM) based on the association between turning parameters and machineability indices. The Moth-Flame Optimization (MFO) algorithm is proposed in this work to identify the optimal set of turning parameters through the MLRM models, in view of minimizing the machinability indices. Three case studies by considering individual machinability indices, a combination of dual indices, and a combination of all three indices, are performed. The suggested MFO algorithm’s effectiveness is evaluated in comparison to the findings of Genetic, Grass-Hooper, Grey-Wolf, and Particle Swarm Optimization algorithms. From the results, it is identified that the MFO algorithm outperformed the others. In addition, a confirmation experiment is conducted to verify the results of the MFO algorithm’s optimal combination of turning parameters.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jaffer Okiring ◽  
Adrienne Epstein ◽  
Jane F. Namuganga ◽  
Victor Kamya ◽  
Asadu Sserwanga ◽  
...  
Keyword(s):  
Regression Models ◽  
Malaria Incidence ◽  
Temporal Changes ◽  
Linear Regression Models ◽  
High Burden ◽  
Exponential Regression ◽  
Malaria Morbidity ◽  
Laboratory Test Results ◽  
Combining Data ◽  
Catchment Areas

Abstract Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

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