FHWA's Maintenance Decision Support System Project

Author(s):  
William P. Mahoney ◽  
Ben Bernstein ◽  
Jamie Wolff ◽  
Seth Linden ◽  
William L. Myers ◽  
...  

The Federal Highway Administration's Office of Transportation Operations Road Weather Management Program began a project in FY 1999 to develop a prototype winter road maintenance decision support system (MDSS). The MDSS capabilities are based on feedback received by the FHWA in 2001 from maintenance managers at a number of state departments of transportation (DOTs) as part of an initiative to capture surface transportation weather decision support requirements. The MDSS project goal is to seed the implementation of advanced decision support services provided by the private sector for state DOTs. This has been achieved by developing core software capabilities that serve as a basis for these tailored products. After the 2001 user needs assessment was completed, the MDSS program was extended with the objective of developing and demonstrating a functional prototype MDSS. Field demonstrations of the prototype MDSS were conducted in Iowa between February and April 2003, and during the winter of 2004. The performance of the prototype MDSS was much improved during the second winter. The weather and road condition predictions were more accurate, and the treatment recommendations generated by the system were reasonable given the predicted conditions. Iowa garage supervisors actively considered the treatment guidance, and on occasion they successfully used the recommended treatments without modification. This paper describes the status of the MDSS project, results and lessons learned from the field demonstrations, and future development efforts.

2019 ◽  
Vol 11 (22) ◽  
pp. 6202 ◽  
Author(s):  
Valentina Zaccaria ◽  
Moksadur Rahman ◽  
Ioanna Aslanidou ◽  
Konstantinos Kyprianidis

The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.


2017 ◽  
Vol 4 (1) ◽  
pp. 88 ◽  
Author(s):  
Agus Perdana Windarto

<p align=""><em>In an industry sales, competition is a natural thing. The number of businesses with the same type makes an entrepreneur should have the right strategies in increasing the purchasing power of customers and reap the benefits. This research aims to implement the algorithms in computer science to create a decision support system for granting rewards to customers Drinking water Depot. In this research method used is TOPSIS and SAW. Where samples are used as much as 6 customers with the assessment criteria is the status of payments, the status of customer liveliness, long subscription, purchase amount, and the time of purchase. From the comparison of the two methods, showed that the calculations carried out by TOPSIS method is better than the SAW method.</em></p><p><strong><em>Keywords</em></strong><em>: Customer, SPK, Reward, TOPSIS method, Method SAW</em></p><p><em>Dalam sebuah industri penjualan, persaingan merupakan hal yang wajar. Banyaknya usaha-usaha dengan jenis yang sama membuat seorang pengusaha harus memiliki strategi-strategi yang tepat dalam meningkatkan daya beli pelanggan dan menuai keuntungan.</em><em> Penelitian ini bertujuan untuk mengimplementasikan algoritma dalam ilmu komputer untuk membuat sistem pendukung keputusan pemberian reward kepada pelanggan Depot Air minum. Dalam penelitian ini metode yang digunakan adalah TOPSIS dan SAW. Dimana sampel yang digunakan sebanyak 6 pelanggan dengan kriteria penilaian adalah </em><em>status pembayaran, status keaktifan pelanggan, lama berlangganan, jumlah pembelian, dan waktu pembelian</em><em>. Dari hasil perbandingan kedua metode tersebut, diperoleh hasil bahwa perhitungan yang dilakukan dengan metode TOPSIS lebih baik dibandingkan dengan metode SAW.</em></p><p><strong><em>Kata Kunci</em></strong><em>: </em><em>Pelanggan</em><em>, SPK, </em><em>Reward</em><em>,</em><em> </em><em>Metode </em><em>TOPSIS</em><em>, </em><em>Metode SAW</em><em></em></p>


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