Estimation of Gear Backlash: Theory and Simulation

1998 ◽  
Vol 120 (1) ◽  
pp. 74-82 ◽  
Author(s):  
Jeffrey L. Stein ◽  
Churn-Hway Wang
Keyword(s):  
Computer Simulation ◽  
Quality Control ◽  
Open Loop ◽  
Measurement Noise ◽  
Model Parameters ◽  
Model Structure ◽  
Estimation Technique ◽  
Structure Model ◽  
Product Quality Control ◽  
Gear Backlash

Machine and product condition monitoring is important to product quality control, especially for unmanned manufacturing. This paper proposes a technique for the estimation of clearance in mechanical systems under dynamic conditions with specific application to the estimation of backlash in gear systems of servomechanisms. The technique is based on a momentum transfer analysis that shows that the change in the speed (defined as bounce) of the primary gear due to impact with the secondary gear is related to the magnitude of the backlash. An algorithm is presented to estimate the bounce in real-time. The algorithm estimates the bounce by computing the standard bounce which is defined as the standard deviation of the demodulated envelope of the primary gear speed. The standard bounce is shown to be a good measure of the bounce when the system is excited sinusoidally. The algorithm’s accuracy and sensitivity are verified through computer simulation of an open-loop DC servomechanism. An approximately linear relationship between the standard bounce and the backlash magnitude is observed. This holds for backlash values exceeding recommended tolerances by ±100 percent. The algorithm is also shown to be insensitive to changes in the simulation model structure, model parameters as well as system and measurement noise. The estimation technique is accurate, computationally simple, and requires no additional sensors if the servosystem to be monitored already has a conventional tachometer.

Product Quality Control Based on Medium Logic

2011 ◽  
Vol 130-134 ◽  
pp. 2470-2475
Author(s):  
Wei Dong Huang ◽  
Yin Mao Liu ◽  
Mei Rong Wu
Keyword(s):  
Computer Simulation ◽  
Quality Control ◽  
Process Control ◽  
Product Quality ◽  
Quality Index ◽  
Measurement Method ◽  
Quality Characteristics ◽  
Product Quality Control ◽  
Quality Detection ◽  
Index Variation

Focusing on representation of quality characteristics variation in product process control, a measurement method based on medium logic was established. Representation model of quality detection index variation in process control was put forward, and fuzziness measurement of quality detection index was improved. Tack monitoring and maintenance of production line were researched by analyzing the variation of product quality index with disturbance. Furthermore, computer simulation was carried out by which influence of controllable variables on product quality with disturbance was analyzed and by which the efficiency of model built above was supported.

scholarly journals Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system

2021 ◽  
Vol 25 (10) ◽  
pp. 5603-5621
Author(s):  
Andrew J. Newman ◽  
Amanda G. Stone ◽  
Manabendra Saharia ◽  
Kathleen D. Holman ◽  
Nans Addor ◽  
...  
Keyword(s):  
Hydrologic Modeling ◽  
Initial Conditions ◽  
Hydrologic Model ◽  
Flood Frequency ◽  
Model Parameters ◽  
Model Structure ◽  
Structure Model ◽  
Return Periods ◽  
Modeling Framework ◽  
Flood Generation

Abstract. This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.

Review of Electric Load Modeling Research

2013 ◽  
Vol 811 ◽  
pp. 627-630 ◽  
Author(s):  
Xue Song Zhou ◽  
Huan Liang ◽  
You Jie Ma
Keyword(s):  
Voltage Stability ◽  
Transient Stability ◽  
Load Flow ◽  
Model Parameters ◽  
Model Structure ◽  
Electric Load ◽  
Structure Model ◽  
Load Modeling ◽  
Load Model ◽  
Model Research

The effect of load model on the analyses of load flow, transient stability, small disturbance stability and voltage stability is analyzed. The importance of the load modeling research is emphasized. The development of component-based method and measurement-based method is reviewed. The advances on the load model research including the select ion of load model structure, model parameters identification, load model with the voltage stability analysis and the sensitivity of load model to the transient stability is summarized.

scholarly journals Identifying Sensitivities in Flood Frequency Analyses using a Stochastic Hydrologic Modeling System

2021 ◽  
Author(s):  
Andrew J. Newman ◽  
Amanda G. Stone ◽  
Manabendra Saharia ◽  
Kathleen D. Holman ◽  
Nans Addor ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Flood Frequency ◽  
Model Parameters ◽  
Model Structure ◽  
Structure Model ◽  
Return Periods ◽  
Hydrologic Modelling ◽  
Modelling Framework ◽  
Flood Generation ◽  
Model Structures

Abstract. This study assesses sources of variance in stochastic hydrologic modelling to support flood frequency analyses. The major components of the modelling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100,000 years at two watersheds representing different hydro-climates across the western United States. Ten hydrologic model structures were configured, calibrated and run within the Framework for Understanding Structural Errors (FUSE) modular modelling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100-member historical meteorology ensemble. A stochastic event-based hydrologic modelling workflow was developed using the calibrated models; millions of flood event simulations were performed at each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. The attribution of the variance of flood frequencies to each component of a stochastic hydrological modelling framework, including several hydrological model structures, is a novel contribution to the flood modelling literature. Results demonstrate that different components of the modelling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare events, while initial conditions are most influential for the more frequent events. However, the hydrological model structure and structure-parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.

SYSTEM APPROACH TO COAL PRODUCT QUALITY CONTROL

2017 ◽  
Vol 17 (6) ◽  
pp. 13-24
Author(s):  
E.V. Freydina ◽  
◽  
E.V. Freydina ◽  
A.A. Botvinnik ◽  
A.N. Dvornikova ◽  
...  
Keyword(s):  
Quality Control ◽  
Product Quality ◽  
System Approach ◽  
Product Quality Control ◽  
Coal Product

Dynamic Modeling of an Unmanned Helicopter From Flight Test Data

2011 ◽  
Author(s):  
Guanlin Wang ◽  
Jihong Zhu ◽  
Hui Xia
Keyword(s):  
Minimum Cost ◽  
Flight Test ◽  
Model Parameters ◽  
Model Structure ◽  
Unmanned Helicopter ◽  
Final Model ◽  
Identification Approach ◽  
Model Set ◽  
Partition Method ◽  
Approach Time

Accurately modeling the dynamic characteristics of a helicopter is difficult and time-consuming. This paper presents a new identification approach which applies the modes partition method and structure traversal (MPM/ST) algorithm. The dynamic modes, instead of model parameters of each model structure, are sequentially identified through MPM. The model with the minimum cost function (CF) is chosen from the best model set and is defined as the final model. Real flight tests of an unmanned helicopter are carried out to verify the identification approach. Time- and frequency-domain results of the identified models clearly demonstrate the potential of MPM/ST in modeling such complex systems.

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