Application of Data Analytics in Gas Turbine Engines

2019 ◽  
Author(s):  
Danteswara Rao Taluru ◽  
Rajendra Prasad Uppara Allabanda
Keyword(s):  
Life Cycle ◽  
Gas Turbine ◽  
Data Analytics ◽  
Product Life Cycle ◽  
Fold Increase ◽  
Aircraft Engine ◽  
Test Case ◽  
Product Life ◽  
The Future ◽  
Using Data

Abstract In recent years, Aerospace technology has seen a paradigm shift towards Data analytics. Design, manufacturing, aftermarket and operations found importance of data analytics in reducing cost. Manufactures are developing Data Analytic based tools which help in optimizing their processes and reduce lead time. Engine, aircraft manufactures along with airliners are working together to improve customer experience. This paper, covers topics related to engine manufactures point of view. Engine manufactures can apply data analytics in concept initiation, concept optimization, preliminary, detailed and validation phases. Manufactures can optimize supply chain management using data analytics. Major gas turbine manufactures are persistent to enhance intelligence in aerospace product life cycle. Data analytics in aerospace engineering also helps in making predictions based upon descriptive patterns from huge data. Since, the aircraft industry is expecting a seven-fold increase in air traffic by 2050. The future demand of aircraft engine production would drive industries to adopt to big data in helping decision making and dynamic production capabilities. This paper helps in identifying different data analytics application in gas turbine product life cycle. Specifically in aero thermal discipline, which would help in increasing efficiency, optimizing design and result in reducing cost. This paper takes the reader through the application of gas turbine as a systematic and concise article. The future scope of the paper would include a test case explaining the application more in detail using data analytics.

scholarly journals Product Life Cycle Analytics – Next Generation Data Analytics on Structured and Unstructured Data

Procedia CIRP ◽  
2015 ◽  
Vol 33 ◽  
pp. 35-40 ◽  
Author(s):  
Laura Kassner ◽  
Christoph Gröger ◽  
Bernhard Mitschang ◽  
Engelbert Westkämper
Keyword(s):  
Life Cycle ◽  
Data Analytics ◽  
Product Life Cycle ◽  
Unstructured Data ◽  
Next Generation ◽  
Product Life

A novel recommendation algorithm based on product life cycle theory

2020 ◽  
pp. 1-20
Author(s):  
Liang Zhang ◽  
Xiao Jing Liu
Keyword(s):  
Life Cycle ◽  
User Satisfaction ◽  
Product Life Cycle ◽  
Recommendation System ◽  
Recommendation Algorithm ◽  
Product Life ◽  
Filtering Algorithm ◽  
Life Cycle Theory ◽  
The Future ◽  
Er Algorithm

A large number of practical applications of the recommendation system found that the novelty of the recommendation results and the user satisfaction are more closely related, making the novelty recommendation recently widely concerned and studied. Many novelty recommendation algorithms used the popularity of the item to measure novelty, but this method is too simple, and the change of item popularity is more reflective of its novelty. According to the product life cycle theory (PLC), this study proposed a novelty recommendation algorithm that recommends item that be not popular now and may be popular in the future to improve the novelty of the recommendation results, The time change of the popularity of the items to be recommended is analyzed, and the future popularity of the items are predicted by analogy. Two strategies for selecting recommended selection are selecting future popular items (the predicting popularity-based filtering Algorithm, PP algorithm) and excluding future recession items (the Excluding Recession-based filtering algorithm, ER algorithm), according to the definition of novelty of the item, recommended the novelty items to the target user. The effectiveness of the proposed algorithm was verified through an offline experiment. Results indicate that PP algorithm can significantly improve the accuracy and novelty, but seriously sacrifice the coverage and reduce the ability of the recommendation system to mine the long tail items when the number of alternative items N is small, the novelty of the recommendation list of the ER algorithm is remarkably higher than that of traditional algorithms, the novelty is high when the quantity of alternative sets reaches around 350, where the average popularity of the recommendation list declines by 40%, and the coverage is elevated by 150%, thereby improving the ability of the proposed system to extract all kinds of items. This study serves as reference for the improvement of user satisfaction with recommendation systems.

scholarly journals The Digital Shadow as Enabler for Data Analytics in Product Life Cycle Management

Procedia CIRP ◽  
2019 ◽  
Vol 80 ◽  
pp. 729-734 ◽  
Author(s):  
Michael Riesener ◽  
Günther Schuh ◽  
Christian Dölle ◽  
Christian Tönnes
Keyword(s):  
Life Cycle ◽  
Data Analytics ◽  
Product Life Cycle ◽  
Life Cycle Management ◽  
Product Life ◽  
Product Life Cycle Management ◽  
Digital Shadow
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