A Prediction Method of the Stability of the Recycling Industry Chain Based on Bayesian Network

2016 ◽  
Author(s):  
Special Issues Editor
Keyword(s):  
Bayesian Network ◽  
Prediction Method ◽  
Recycling Industry ◽  
Industry Chain ◽  
The Stability

Stability Prediction of Surrounding Rocks and Optimum Design of Roof-Bolt Parameters in Deep Roadway

2013 ◽  
Vol 353-356 ◽  
pp. 436-439
Author(s):  
De Sen Kong ◽  
Yong Po Chen
Keyword(s):  
Prediction Method ◽  
Simulation Method ◽  
Complex Stress ◽  
Roof Bolt ◽  
Deformation And Failure ◽  
Long Run ◽  
Research Findings ◽  
Stress Environment ◽  
Classification Prediction ◽  
The Stability

In order to forecast the stability of deep roadway and optimize the parameters of bolts, the complex stress environment and the multivariate surrounding rocks characteristics of deep roadway were analyzed. Then the classification prediction method and the numerical simulation method were simultaneously used to analysis the stability of surrounding rocks. Furthermore, the supporting parameters of bolts were also designed optimally. It was shown that the characteristics of stress distribution, deformation and failure zone of surrounding rocks are not ideal. So it is necessary to optimize the supporting parameters of deep roadway. All these research findings will provide the theory basis for bolts of deep roadway and will ensure the optimization of bolts and the stability of deep roadway in the long run.

Analytical Chatter Stability of Milling With Rotating Cutter Dynamics at Process Damping Speeds

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
M. Eynian ◽  
Y. Altintas
Keyword(s):  
Chip Thickness ◽  
Prediction Method ◽  
Vibration Velocity ◽  
Chatter Stability ◽  
Process Damping ◽  
End Mills ◽  
Nyquist Stability ◽  
Matrix Differential ◽  
The Stability ◽  
Delay Matrix

This paper presents a chatter stability prediction method for milling flexible workpiece with end mills having asymmetric structural dynamics. The dynamic chip thickness regenerated by the vibrations of the rotating cutter and the fixed workpiece is transformed into the principle modal directions of the rotating tool. The process damping is modeled as a linear function of vibration velocity. The dynamics of the milling system is modeled by a time delay matrix differential equation with time varying directional factors and speed dependent elements. The periodic directional factors are averaged over a spindle period, and the stability of the resulting time invariant but speed dependent characteristic equation of the system is investigated using the Nyquist stability criterion. The stability model is verified with time domain numerical simulations and milling experiments.

scholarly journals A Novel Multiscale Deep Health Indicator with Bidirectional LSTM Network for Bearing Performance Degradation Trend Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Han Wang ◽  
Gang Tang ◽  
Youguang Zhou ◽  
Yujing Huang
Keyword(s):  
Short Term Memory ◽  
Prediction Method ◽  
Health Indicators ◽  
Performance Degradation ◽  
Statistical Characteristics ◽  
Health Indicator ◽  
Trend Prediction ◽  
Lstm Network ◽  
Novel Strategy ◽  
The Stability

As rolling bearings are the key components in rotating machinery, bearing performance degradation directly affects machine running status. A tendency prognosis for bearing performance degradation is thus required to ensure the stability of operation. This paper proposes a novel strategy for bearing performance degradation trend prognosis, including health indicator construction techniques and a performance degradation trend prediction method. To more accurately represent the degradation trend, the multiscale deep bottleneck health indicator is proposed as a new synthesized health indicator to remove high-frequency detail signals from features, which can reduce possible fluctuations in conventional synthetic health indicators. A suitable method for selecting the statistical characteristics required for fusion is also presented to solve the problem of information redundancy that affects trend representation. In addition, a stacked autoencoder network is used for deep feature extraction of selected statistical features. A bidirectional long short-term memory network prediction model is also proposed for the prediction of degradation trend, which can make full use of historical and future information to improve prediction accuracy. Finally, experiments are carried out to verify the effectiveness of the proposed method.

The Application of Gray Theory on Predicting the Settlement of High Embankment

2013 ◽  
Vol 671-674 ◽  
pp. 1272-1276
Author(s):  
Ju Jin ◽  
Yuan Ming Dou ◽  
Qi Nan Li
Keyword(s):  
Prediction Method ◽  
Normal Operation ◽  
Dynamic Prediction ◽  
Settlement Prediction ◽  
The Road ◽  
Gray Theory ◽  
Operation And Maintenance ◽  
Predicted Model ◽  
Strict Requirement ◽  
The Stability

Stability of the road embankment is the foundation of the highway normal operation and maintenance, so there is strict requirement of road subgrade settlement for the highway. This article uses the gray theory to predict Cheng Chi high embankment construction settlement and builds a predicted model and tests the model’s accuracy. The results of settlement prediction are under the limits, so the stability of the road subgrade is good. This dynamic prediction method can be used on prediction of the settlement of high embankment and it’ll direct the operation and ensure the maintenance safety.

scholarly journals Study on Prediction Method for Postconstruction Settlement of Loess Fill Foundation in Northern Shaanxi, China

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Kui Liu ◽  
Kangze Yuan ◽  
Boyi Shi
Keyword(s):  
Evaluation Criteria ◽  
Prediction Method ◽  
Summation Method ◽  
Power Functions ◽  
Stress Strain ◽  
Creep Equation ◽  
Test Analysis ◽  
Northern Shaanxi ◽  
The Stability ◽  
Relationship Of

In order to solve the problem of shortage of construction land in the loess hilly and gully area of northern Shaanxi, the local people usually adopted the method of excavating and filling. The postconstruction settlement was an important index to evaluate the stability of the loess fill foundation. Through laboratory test analysis, the stress-strain and the strain-time relationship of compacted loess were obtained. It showed that the stress-strain curves varied as power functions, and the relationship between strain and time was hyperbolic. Based on the layerwise summation method, a creep equation to predict the postconstruction settlement of loess fill foundation was established. The field monitoring data show that the fitting effect is better. Using this equation, the postconstruction settlement of loess fill foundation with different compaction coefficients and thickness was predicted. Finally, the stability evaluation criteria of loess fill foundation with various thickness and compaction coefficient were proposed. This method provided a new idea to solve the problem of postconstruction settlement of loess fill foundation.

scholarly journals A Bearing Life Prediction Method of Improving Smooth Degree and the Background Value

2021 ◽  
Vol 236 ◽  
pp. 02006
Author(s):  
Yichi Zhang ◽  
Yan Xu ◽  
Tao Shu
Keyword(s):  
Model Prediction ◽  
Life Prediction ◽  
Working Condition ◽  
Prediction Method ◽  
Background Value ◽  
Bearing Life ◽  
Working Performance ◽  
The Stability ◽  
The Moment ◽  
New Formula

Bearings, as a component in many complex weapons, can be used to reduce friction to improve the efficiency of equipment. Bearing CV value can quantify the working performance of bearings, which can act as a reference standard for staff to evaluate the working condition of bearings. According to the known data, the real CV value of the bearing is calculated in this paper. In order to improve the smoothing ratio, the data is processed by the idea of data transformation and the background value is optimized by the new formula. The two improve the GM (1,1) model and simulate the predicted bearing CV and calculate the moment of failure by this model, which is compared with the traditional GM (1,1) and the improved GM (1,1) by cumulative method in terms of error and accuracy. It is verified that the average relative error and the model prediction accuracy of the model prediction life are 0.0185 and 98.15% respectively after the improvement of the stability and background value. Therefore, this method has certain practical value in engineering, and is more effective than the cumulative GM (1,1) model.

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