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Intuitionistic fuzzy-based model for failure detection

Overview of attention for article published in SpringerPlus, November 2016
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1 tweeter

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Title
Intuitionistic fuzzy-based model for failure detection
Published in
SpringerPlus, November 2016
DOI 10.1186/s40064-016-3446-0
Pubmed ID
Authors

Daniel O. Aikhuele, Faiz B. M. Turan, Daniel O. Aikhuele, Faiz B. M. Turan

Abstract

In identifying to-be-improved product component(s), the customer/user requirements which are mainly considered, and achieved through customer surveys using the quality function deployment (QFD) tool, often fail to guarantee or cover aspects of the product reliability. Even when they do, there are always many misunderstandings. To improve the product reliability and quality during product redesigning phase and to create that novel product(s) for the customers, the failure information of the existing product, and its component(s) should ordinarily be analyzed and converted to appropriate design knowledge for the design engineer. In this paper, a new intuitionistic fuzzy multi-criteria decision-making method has been proposed. The new approach which is based on an intuitionistic fuzzy TOPSIS model uses an exponential-related function for the computation of the separation measures from the intuitionistic fuzzy positive ideal solution (IFPIS) and intuitionistic fuzzy negative ideal solution (IFNIS) of alternatives. The proposed method has been applied to two practical case studies, and the result from the different cases has been compared with some similar computational approaches in the literature.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 32%
Lecturer 2 11%
Student > Postgraduate 2 11%
Professor 1 5%
Student > Bachelor 1 5%
Other 4 21%
Unknown 3 16%
Readers by discipline Count As %
Engineering 4 21%
Business, Management and Accounting 3 16%
Decision Sciences 2 11%
Social Sciences 1 5%
Chemical Engineering 1 5%
Other 0 0%
Unknown 8 42%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 September 2017.
All research outputs
#7,287,514
of 11,707,803 outputs
Outputs from SpringerPlus
#820
of 1,703 outputs
Outputs of similar age
#150,234
of 263,625 outputs
Outputs of similar age from SpringerPlus
#1
of 2 outputs
Altmetric has tracked 11,707,803 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,703 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,625 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them