scholarly journals EVENT ORIENTED MODELING RESTORING OPERABILITY OF LARGE-SCALE SOCIO-TECHNICAL SYSTEMS

2017 ◽  
pp. 161-170
Author(s):  
В.Д. БОЙКО
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Component Model ◽  
Four Component Model ◽  
Technical Systems

This paper introduces an event-oriented four-component model for analyzing the efficiency of the fault diagnosis process. The simulation of the process of diagnostics and recovery of complex socio-technical systems allows for modeling either blind, either directed troubleshooting strategies. Also paper introduces criteria for the appropriateness of the intro-duction of diagnostic and troubleshooting systems with relating to diagnosing systems and the context of their use.

scholarly journals Identifying Mixture Components From Large-Scale Keystroke Log Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Tingxuan Li
Keyword(s):  
Large Scale ◽  
Model Comparison ◽  
Writing Process ◽  
Component Model ◽  
Log Data ◽  
Four Component Model ◽  
Wide Range ◽  
Writing Genres ◽  
Educational Testing Service ◽  
Real Time Information

In a computer-based writing assessment, massive keystroke log data can provide real-time information on students’ writing behaviors during text production. This research aims to quantify the writing process from a cognitive standpoint. The hope is that the quantification may contribute to establish a writing profile for each student to represent a student’s learning status. Such profiles may contain richer information to influence the ongoing and future writing instruction. Educational Testing Service (ETS) administered the assessment and collected a large sample of student essays. The sample used in this study contains nearly 1,000 essays collected across 24 schools in 18 U.S. states. Using a mixture of lognormal models, the main findings show that the estimated parameters on pause data are meaningful and interpretable with low-to-high cognitive processes. These findings are also consistent across two writing genres. Moreover, the mixture model captures aspects of the writing process not examined otherwise: (1) for some students, the model comparison criterion favored the three-component model, whereas for other students, the criterion favored the four-component model; and (2) students with low human scores have a wide range of values on the mixing proportion parameter, whereas students with higher scores do not possess this pattern.

Using Rest's Four Component Model to Predict Applicant Cheating

2011 ◽  
Author(s):  
Lynn R. Hartmann ◽  
Kristie Lynn Campana ◽  
Lance Andrews
Keyword(s):  
Component Model ◽  
Four Component Model

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

scholarly journals Validity of two-component models for estimating body fat of black men

2001 ◽  
Vol 90 (2) ◽  
pp. 649-656 ◽  
Author(s):  
Dale R. Wagner ◽  
Vivian H. Heyward
Keyword(s):  
Black Men ◽  
Body Fat ◽  
Predictive Accuracy ◽  
Component Model ◽  
Suitable Alternative ◽  
Heterogeneous Sample ◽  
Four Component Model ◽  
Two Component ◽  
Model Conversion ◽  
Component Models

Commonly used two-component model conversion formulas that estimate relative body fat (%BF) from body density (Db) were cross-validated on a heterogeneous sample of black men ( n = 30; age = 19–45 yr). A four-component model was used to obtain criterion measures of %BF, and linear regression and analysis of individual residual scores were conducted to assess the predictive accuracy of the formulas under investigation. The two-component formula commonly used to estimate %BF of black men (Schutte JE, Townsend EJ, Hugg J, Shoup RF, Malina RM, and Blomqvist CG. J Appl Physiol 56: 1647–1649, 1984) significantly ( P ≤ 0.01) and systematically (87% of sample) overestimated %BF (−1.28%); thus we developed the following two-component Db conversion formula: %BF = [(4.858/Db) − 4.394] × 100. Because our formula was derived from a four-component model and a larger, more heterogeneous sample than the commonly used two-component formula, we recommend using it to convert Db to %BF for black men. Additionally, there was good agreement between dual-energy X-ray absorptiometry and the four-component model, making this a suitable alternative for estimating the %BF of black men.

Fault-Diagnosis Architecture for Large-Scale Photovoltaic Power Plants That Does Not Require Additional Sensors

2019 ◽  
Vol 9 (3) ◽  
pp. 780-789 ◽  
Author(s):  
Tohru Kohno ◽  
Kenichi Gokita ◽  
Hideyuki Shitanishi ◽  
Masahito Toyosaki ◽  
Tomoharu Nakamura ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Plants ◽  
Large Scale ◽  
Photovoltaic Power

scholarly journals Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3396 ◽  
Author(s):  
Mingzhu Tang ◽  
Wei Chen ◽  
Qi Zhao ◽  
Huawei Wu ◽  
Wen Long ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Support Vector Regression ◽  
Confidence Intervals ◽  
Wind Turbines ◽  
Large Scale ◽  
Support Vector ◽  
Effective Response ◽  
Support Vector Regression Model ◽  
Doubly Fed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

Sign in / Sign up

Export Citation Format

Share Document