scholarly journals Swing Characteristics and Vibration Feature of Tower Cranes under Compound Working Condition

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Fu Liu ◽  
Jianwei Yang ◽  
Jinhai Wang ◽  
Changdong Liu
Keyword(s):  
Root Mean Square ◽  
Working Conditions ◽  
Dynamic Models ◽  
Frequency Standard ◽  
Parameter Analysis ◽  
Structural Vibration ◽  
Mean Square ◽  
Swing Angle ◽  
Coupling Motion ◽  
The Time Domain

The swing behaviour of tower cranes under compound working conditions is closely related to construction safety and structural health. This paper presents dynamic models and simulated them for parameter analysis to understand tower cranes’ dynamic characteristics and vibration features under compound working conditions. The parameters contain payload mass, rope length, lifting acceleration, slewing acceleration, luffing acceleration, and initial angle. For the lifting-luffing coupling motion (LLCM) and lifting-slewing coupling motion (LSCM) of the tower crane, the D’Alembert principle provides a theoretical basis for the derivation of system dynamics equations. The spatial swing angle description of the crane payload includes the time-domain response and frequency-domain response, which uses a dynamic model. The result shows that the mass has little effect on the spatial swing angle. The value of the lifting acceleration is stable at 0.004 m/s2 to 0.01 m/s2. Peak value (PV), root mean square value (RMS), root mean square frequency (RMSF), and frequency standard deviation (RVF) present the best sensitivity to changes in the spatial swing angle response. When PV of angles θ and β increases by tens of thousands of times in the LLCM, PV can reflect the phenomenon of angle divergence. The skewness value (SV) increases by 3422% at the severe swing angle performance in the LSCM. The swing angle regularity with the compound working conditions can provide theoretical guidance for eliminating structural vibration.

Dynamic monitoring feedback iteration control for damping-adjustable semi-active suspension systems

2021 ◽  
pp. 1-9
Author(s):  
Chongchong Li ◽  
Jiangyong Xiong ◽  
Tingshan Liu ◽  
Ziang Zhang
Keyword(s):  
Root Mean Square ◽  
Working Conditions ◽  
Control Algorithm ◽  
Active Suspension ◽  
Dynamic Monitoring ◽  
Iteration Algorithm ◽  
Mean Square ◽  
Control Effect ◽  
Analytic Hierarchy ◽  
Vehicle Simulation

In order to further improve vehicle ride performance, a dynamic monitoring feedback iteration control algorithm is proposed by combining the features of a variable-damping semi-active suspension system and applying them to the system. A seven-degree-of-freedom finished vehicle simulation model is built based on MATLAB/Simulink. The root-mean-square values of the acceleration of the sprung mass, the dynamic travel of the suspension and the dynamic tire load are taken as evaluation indicators of vehicle ride performance. An analytic hierarchy process (AHP) is used to determine the weighting coefficients of the evaluation indicators, and a genetic algorithm is utilized to determine the optimal damping of the suspension under various typical working conditions. Suspension damping is controlled with a dynamic monitoring feedback iteration algorithm. The correction coefficients of the control algorithm are determined according to the deviation between the obtained damping and the optimized damping so that the control parameters will agree with the optimal result under typical working conditions, and the control effect under other working conditions is verified. The simulation results indicate that the proposed dynamic monitoring feedback iteration control algorithm can effectively reduce the root-mean-square value of the acceleration of the sprung mass by 10.56% and the root-mean-square value of the acceleration of the dynamic travel of the suspension by 11.98% under mixed working conditions, thus improving vehicle ride performance. The study in this paper provides a new attempt for damping control of semi-active suspension and lays a theoretical foundation for its application in engineering.

scholarly journals Remaining useful tool life predictions in turning using Bayesian inference

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Jaydeep M. Karandikar ◽  
Ali Abbas ◽  
Tony L. Schmitz
Keyword(s):  
Bayesian Inference ◽  
Tool Wear ◽  
Root Mean Square ◽  
Tool Life ◽  
Sample Path ◽  
Growth Curves ◽  
Mean Square ◽  
The Time Domain ◽  
Machining Productivity ◽  
Spindle Power

Tool wear is an important factor in determining machining productivity. In this paper, tool wear is characterized by remaining useful tool life in a turning operation and is predicted using spindle power and a random sample path method of Bayesian inference. Turning tests are performed at different speeds and feed rates using a carbide tool and MS309 steel work material. The spindle power and the tool flank wear are monitored during cutting; the root mean square of the time domain power is found to be sensitive to tool wear. Sample root mean square power growth curves are generated and the probability of each curve being the true growth curve is updated using Bayes’ rule. The updated probabilities are used to determine the remaining useful tool life. Results show good agreement between the predicted tool life and the empirically-determined true remaining life. The proposed method takes into account the uncertainty in tool life and the growth of the root mean square power at the end of tool life and is, therefore, robust and reliable.

Remaining Useful Tool Life Predictions Using Bayesian Inference

2013 ◽  
Author(s):  
Jaydeep Karandikar ◽  
Tom McLeay ◽  
Sam Turner ◽  
Tony Schmitz
Keyword(s):  
Bayesian Inference ◽  
Tool Wear ◽  
Root Mean Square ◽  
Tool Life ◽  
End Milling ◽  
Growth Curves ◽  
Mean Square ◽  
The Time Domain ◽  
Life Predictions ◽  
Machining Productivity

Tool wear is an important limitation to machining productivity. In this paper, remaining useful tool life predictions using the random walk method of Bayesian inference is demonstrated. End milling tests were performed on a titanium workpiece and spindle power was recorded. The power root mean square value in the time domain was found to be sensitive to tool wear and was used for tool life predictions. Sample power root mean square growth curves were generated and the probability of each curve being the true growth curve was updated using Bayes’ rule. The updated probabilities were used to determine the remaining useful tool life. Results show good agreement between the predicted tool life and the true remaining life. The proposed method takes into account the uncertainty in tool life and the percentage of nominal power root mean square value at the end of tool life.

scholarly journals Development of a hand-held magnetic resonance sensor for the nondestructive quantification of fat and lean meat of fresh tuna

2020 ◽  
Vol 14 (6) ◽  
pp. 2947-2955
Author(s):  
Yoshito Nakashima
Keyword(s):  
Magnetic Resonance ◽  
Root Mean Square ◽  
Time Domain ◽  
Subcutaneous Fat ◽  
Mean Square ◽  
Fat Quantification ◽  
Lean Meat ◽  
Root Mean Square Residual ◽  
Resonance Sensor ◽  
The Time Domain

Abstract For the in-situ nondestructive fat quantification of fresh tuna meat, an original lightweight (5.7 kg) hand-held sensor that consists of a planar radio-frequency coil and a single-sided magnetic circuit was developed as a subunit of a time-domain proton magnetic resonance (MR) scanner system. The investigation depth of the sensor unit is 12 mm, which is sufficient to probe the meat section beneath thick skin with scales and the underlying subcutaneous fat layer of large fish such as tuna. The scanner was successfully applied in a laboratory to a fillet of a bluefin tuna (Thunnus thynnus) to measure meat sections 12 mm beneath the skin. The required measurement time was 100 s for each section. The results of MR scan at 11 locations on the fillet were compared with those of conventional destructive food analysis. Reasonable agreement with an error (root-mean-square residual) of as small as 1.8 wt% was obtained for fat quantification. The time-domain MR relaxometry for the same tuna fillet also allowed lean meat quantification with a small root-mean-square residual of 6.7 wt%.

The Relationship of Root Mean Square Value of Electromyography and Isometric Torque of Quadriceps in Normal Subjects

2016 ◽  
Vol 26 (1) ◽  
pp. 58
Author(s):  
Qiurong XIE ◽  
Zheng JIANG ◽  
Qinglu LUO ◽  
Jie LIANG ◽  
Xiaoling WANG ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Normal Subjects ◽  
Mean Square ◽  
Isometric Torque ◽  
Relationship Of ◽  
The Relationship

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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