A Maintenance Time Prediction Method based on Modular Arrangement of Predetermined Time Standard and Maintenance Interfaces

2020 ◽  
Author(s):  
Chao Dai ◽  
Dong Zhou ◽  
Yu Fan ◽  
Huan Zhang ◽  
Dianxin Zhao
Keyword(s):  
Prediction Method ◽  
Time Prediction ◽  
Maintenance Time ◽  
Time Standard ◽  
Predetermined Time

The method of predicting the maintenance time of mechanical products based on Virtual Prototype

2022 ◽  
Vol 15 ◽  
Author(s):  
Jin Li ◽  
Xinsheng Jiang ◽  
Zituo Wang ◽  
Chunhui Wang ◽  
Yunxiong Cai
Keyword(s):  
Body Movement ◽  
Estimation Method ◽  
Prediction Method ◽  
Virtual Prototype ◽  
Time Prediction ◽  
Maintenance Time ◽  
Mechanical Products ◽  
Physical Prototype ◽  
Virtual Maintenance ◽  
Maintenance Process

Aim: To predict the mechanical product maintenance time is difficult in the situation of lack of physical prototype or similar products’ statistics in stage of design Method: According to the theory of time accumulative estimation method, a product maintenance time prediction method framework based on virtual prototype was constructed, which described the prediction process. The virtual maintenance environment which contains virtual prototype, virtual human and maintenance tools was developed. The virtual human’s position and posture information during the maintenance process was obtained by implementing VBScript language. Result: Basic maintenance motions that constitute the whole maintenance process were classified into 4 categories: body movement, upper limb movement, grasp/replace and operation. Based on MODAPTS (Modular arrangement of predetermined time standard) method and virtual maintenance simulation, corresponding time prediction methods for each categories were proposed. Discussion: Take a maintenance dissassembly and assembly task of engine as an example, through the comparison between the measured actual maintenance time and predicted time of several methods, feasibility and effectiveness of proposed method are verified

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

Real-Time Prediction Method for Performance Degradation Trend Based on Reliability Experimental Data

2013 ◽  
Vol 321-324 ◽  
pp. 757-761 ◽  
Author(s):  
Chen Liang Song ◽  
Zhen Liu ◽  
Bin Long ◽  
Cheng Lin Yang
Keyword(s):  
Experimental Data ◽  
Real Time ◽  
Field Data ◽  
Prediction Method ◽  
Field Method ◽  
Performance Degradation ◽  
New Method ◽  
Time Prediction ◽  
Prediction Result ◽  
The Relationship

According to the real-time prediction for performance degradation trend, the commonly used method is just based on field data. But this methods prediction result will not be so much ideal when the fitting of degradation trend of field data is not good. To solve the problem, the paper introduces a new method which is not only based on field method but also based on reliability experimental data coming from the history experiment. We use the relationship between the field data and reliability experimental data to get the result of the two kinds of data respectively and then get the weights according to the two prediction results. Finally, the final real-time prediction result for performance degradation tendency can obtain by allocating the weights to the two prediction results.

scholarly journals A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 42372-42383 ◽  
Author(s):  
Qingwen Han ◽  
Ke Liu ◽  
Lingqiu Zeng ◽  
Guangyan He ◽  
Lei Ye ◽  
...  
Keyword(s):  
Arrival Time ◽  
Prediction Method ◽  
Time Prediction ◽  
Position Calibration ◽  
Arrival Time Prediction ◽  
Bus Arrival Time Prediction
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