scholarly journals Short-term Performance Prediction Model for Aviation Job-shop

2014 ◽  
Vol 7 (7) ◽  
pp. 1442-1447
Author(s):  
Ibrahim Y. Sokar ◽  
Yusoff Jamaluddin ◽  
Mardina Abdullah
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Job Shop ◽  
Short Term ◽  
Term Performance

Performance of Flexible and Rigid Pavements in Ohio

1996 ◽  
Vol 1536 (1) ◽  
pp. 94-102
Author(s):  
Hamid Mukhtar ◽  
Osama Abdulshafi
Keyword(s):  
Performance Prediction ◽  
Performance Data ◽  
Functional Classification ◽  
Short Term ◽  
Rigid Pavement ◽  
Rigid Pavements ◽  
And Performance ◽  
Variance Estimates ◽  
Term Performance ◽  
Prediction Parameters

Deviations in traffic and performance prediction parameters and overall standard deviations applicable to Ohio were determined. Pavement test sites were selected to represent the statewide distribution of pavement designs in Ohio, characterized by such factors as material type, functional classification, and different climatic and soil regions. Core samples were obtained and several laboratory tests were conducted to determine the as-constructed material properties and variability of the design input parameters. Comparison of predicted and observed performances based on approximately 4 years of data indicated that the AASHTO equation does not predict the performance of flexible pavements in Ohio. The predicted and the observed performances for rigid pavement sites were essentially the same, that is, no change in the observed and the predicted pavement serviceability index (PSI); however, these observations were based on short-term performance data. The overall variance estimates for flexible and rigid pavements were not obtained due to lack in the change of performance data for most sections.

scholarly journals Framework of Prediction Model for Mid- to Long-Term Performance Changes of Urban Railway Facilities Based on Performance Evaluation Reports

2021 ◽  
Vol 13 (23) ◽  
pp. 13397
Author(s):  
Jonghyeob Kim ◽  
Jae-Goo Han ◽  
Goune Kang ◽  
Kyung-Ho Chin
Keyword(s):  
Performance Evaluation ◽  
Prediction Model ◽  
Performance Prediction ◽  
Trade Off ◽  
Future Performance ◽  
Predicting Performance ◽  
Long Term Performance ◽  
Term Performance ◽  
Term Maintenance

To maintain railway facilities in an appropriate state, systematic management based on mid- and long-term maintenance plans through future performance prediction must be carried out. To this end, it is necessary to establish and utilize a model that can predict mid- to long-term performance changes of railway facilities by predicting performance changes of individual sub-facilities. However, predicting changes in the performance of all sub-facilities can be difficult as it requires large volumes of data, and railway facilities are a collection of numerous sub-facilities. Therefore, in this study, a framework for a model that can predict mid- to long-term performance changes of railway facilities through analysis of continuously accumulated performance evaluation results is proposed. The model is a system with a series of flows that can classify performance evaluation results by individual sub-facilities, predict performance changes by each sub-facility using statistical methods, and predict mid- to long-term performance changes of the facility. The developed framework was applied to 36,537 sub-facilities comprising 12 lines of two urban railways in South Korea to illustrate the model and verify its applicability and effectiveness. This study contributes in terms of its methodology in establishing a framework for predicting mid- to long-term performance changes, providing the basis for the development of an automated model able to continuously predict performance changes of individual sub-facilities. In practical terms, it is expected that railway facility managers who allow trade-off between reliability and usability can contribute to establishing the mid- to long-term maintenance plans by utilizing the model proposed in this study, instead of subjectively building them.

Project performance prediction model linking agility and flexibility demands to project type

2021 ◽  
Author(s):  
Marco Aurélio Oliveira ◽  
Luiz V. O. Dalla Valentina ◽  
André Hideto Futami ◽  
Osmar Possamai ◽  
Carlos Alberto Flesch
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Project Performance

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

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