Integrated framework for constrained minimum-time trajectory generation, fault detection and reconfiguration: A case-study

2015 ◽  
Vol 30 (7) ◽  
pp. 986-1001
Author(s):  
Fajar Suryawan ◽  
José De Doná ◽  
María Seron
Keyword(s):  
Fault Detection ◽  
Trajectory Generation ◽  
Minimum Time ◽  
Integrated Framework

Developing Schedule With Linear Programming (Case Study: STTF II Project Komplek Sukamukti Banjaran)

2020 ◽  
Vol 4 (02) ◽  
pp. 34-45
Author(s):  
Naufal Dzikri Afifi ◽  
Ika Arum Puspita ◽  
Mohammad Deni Akbar
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Project Scheduling ◽  
Time Limit ◽  
Minimum Time ◽  
Service Cost ◽  
Trade Off ◽  
Overtime Work ◽  
The Cost

Shift to The Front II Komplek Sukamukti Banjaran Project is one of the projects implemented by one of the companies engaged in telecommunications. In its implementation, each project including Shift to The Front II Komplek Sukamukti Banjaran has a time limit specified in the contract. Project scheduling is an important role in predicting both the cost and time in a project. Every project should be able to complete the project before or just in the time specified in the contract. Delay in a project can be anticipated by accelerating the duration of completion by using the crashing method with the application of linear programming. Linear programming will help iteration in the calculation of crashing because if linear programming not used, iteration will be repeated. The objective function in this scheduling is to minimize the cost. This study aims to find a trade-off between the costs and the minimum time expected to complete this project. The acceleration of the duration of this study was carried out using the addition of 4 hours of overtime work, 3 hours of overtime work, 2 hours of overtime work, and 1 hour of overtime work. The normal time for this project is 35 days with a service fee of Rp. 52,335,690. From the results of the crashing analysis, the alternative chosen is to add 1 hour of overtime to 34 days with a total service cost of Rp. 52,375,492. This acceleration will affect the entire project because there are 33 different locations worked on Shift to The Front II and if all these locations can be accelerated then the duration of completion of the entire project will be effective

scholarly journals Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis

2021 ◽  
Author(s):  
K Ramakrishna Kini ◽  
Muddu Madakyaru
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Noisy Environment ◽  
Process Data ◽  
Multi Scale ◽  
Efficient Information ◽  
Non Gaussian

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

Urban Fault Detection based on AETA: A Case Study on Line 9 Subway of Shenzhen, China

2021 ◽  
Author(s):  
Shanshan Yong ◽  
Qinmeng Guo ◽  
Xin'An Wang ◽  
Jing Wang ◽  
Chao Yang ◽  
...  
Keyword(s):  
Fault Detection ◽  
On Line

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

Proposing a basic methodology for developing balanced scorecard by system dynamics approach

Kybernetes ◽  
2015 ◽  
Vol 44 (6/7) ◽  
pp. 1049-1066 ◽  
Author(s):  
Seyed Behnam Khakbaz ◽  
Nastaran Hajiheydari
Keyword(s):  
System Dynamics ◽  
Balanced Scorecard ◽  
Public Transportation ◽  
Time Delays ◽  
Scenario Planning ◽  
Content Type ◽  
Integrated Framework ◽  
Management Scenario ◽  
Transportation Company

Purpose – Successful future has inspired organizations to measure long-term and non-financial measurements and key performance indicators (KPIs). Kaplan and Norton proposed balanced scorecard (BSC) for this issue and have extended it to one of the most preferred strategic management system’s tools. However, available planning tools like BSC have some limitations, like dependency to the developer, weakness in showing time delays, and also mathematical relationships between lead and lag indicators. In this paper, the authors would present a new methodology for developing BSCs, which would be able to overcome these limitations. Therefore, the purpose of this paper is to develop an integrated framework for developing BSC with system dynamics approach (a dynamic BSC (DBSC)) which has lower limitation in compare with traditional BSC. The other purpose of this paper is developing a DBSC for an Iranian public transportation company. Design/methodology/approach – Based on this purpose, related literature was thoroughly reviewed and the proposed methodology designed using the system dynamics and BSC concepts. This methodology is a composition of original BSC development methodology and system dynamics principles. An assumed organization has been used for showing methodology’s capability and procedure. Furthermore, a case study has been accomplished in this paper. This case study is a DBSC which has been developed for an Iranian public transportation company. The purpose of this case study is to ensure about proposed methodology implication in action. Findings – The authors proposed a methodology which can be applied for developing BSCs. This methodology consists of six different steps which are: developing a system for organization, selecting stakeholders’ most important objectives and target, identifying organization’s objectives and their KPIs for different BSC aspects, developing strategy map, targeting, and selecting initiatives. In the proceeding of this paper, the proposed methodology and its steps would be explained in detail. Originality/value – The system dynamic approach has precedents in business studies; however, this research makes this approach operational in BSC designing and analysis. BSCs, which developed by this methodology can show time delays between an organization’s objectives, its KPIs’ relationship and also planning for it. Selecting achievable and rational vision and objectives’ targets, change management, scenario planning and policy analysis are other values which can be achieved by DBSC deployment which need further researches. In summary, this research has shown an integrated framework for developing DBSC and then applies it to an Iranian public transportation company. Therefore, another contribution of this paper is the application of this method for an Iranian public transportation company.

Case study results: fault detection in air-handling units in buildings

2018 ◽  
Vol 14 (3) ◽  
pp. 305-321 ◽  
Author(s):  
Suhrid Deshmukh ◽  
Leon Glicksman ◽  
Leslie Norford
Keyword(s):  
Fault Detection ◽  
Study Results ◽  
Air Handling Units
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