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Applied Statistics in Engineering
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Applied Statistics in Engineering

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 12159

Special Issue Editors

Dr. Davide Astolfi

E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: wind turbines; condition monitoring; fault diagnosis; non-stationary machinery; control and monitoring; vibrations; applied statistics; numerical modelling; mechanical systems dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Silvia Cascianelli

E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Interests: machine learning

Special Issue Information

Dear Colleagues,

The recent ubiquitous development of sensors and actuators in advanced technology manufactured products has augmented the importance of data mining and statistical methods in engineering sciences.

On these grounds, the objective of this Special Issue is collecting scientific contributions on applied statistics in every branch of engineering. Test case discussions and practical applications of advanced statistical methods are particularly welcome. Theoretical advances in statistics, with particular relevance for engineering sciences, are welcome too.

Statistical methods of interest for this Special Issue include, but are not limited to, the following:

  • Regression analysis
  • Multivariate analysis
  • Confidence Intervals
  • Time-series analysis
  • Data mining
  • Data classification and clustering
  • Statistical inference
  • Distribution theory
  • Error analysis

Dr. Davide Astolfi
Dr. Silvia Cascianelli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Stats is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Engineering
  • Applied Statistics
  • Multivariate Analysis
  • Regression
  • Time Series
  • Inference
  • Quality Control
  • Process Control
  • Control and Monitoring
  • Reliability
  • System Identification

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

9 pages, 2387 KiB  
Article
A Statistical Approach to Analyzing Engineering Estimates and Bids
by Roshanak Farshidpour, Kiana Negoro and Fariborz M. Tehrani
Stats 2021, 4(1), 62-70; https://doi.org/10.3390/stats4010005 - 13 Jan 2021
Cited by 3 | Viewed by 4871
Abstract
This paper introduces a methodology to assess the accuracy of engineering estimates in relation to the final project cost. The objective of this assessment is to develop a comprehensive approach towards obtaining a more reliable estimate of the project cost. This approach relies [...] Read more.
This paper introduces a methodology to assess the accuracy of engineering estimates in relation to the final project cost. The objective of this assessment is to develop a comprehensive approach towards obtaining a more reliable estimate of the project cost. This approach relies on the review of a synthesis of literature, which provides a basis for determining key components in the estimation of the capital cost of a project. A systematic review of existing data for selected projects was obtained as well. Employed data cover sampled public transportation projects to maintain existing infrastructure within selected geographical location and specified time. Enhanced analysis of existing data through statistical models was employed to indicate potential measures for prevention of errors in the estimate due to uncertainties in the time, cost, and method of construction. The comparison of results with similar findings from past research shows the effectiveness of presented methodologies and opportunities to enhance statistical analyses of bids and engineering estimates. Conclusions enable project managers to address uncertainties in the bidding process and enhance financial sustainability of projects within specific programs. Full article
(This article belongs to the Special Issue Applied Statistics in Engineering)
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16 pages, 3649 KiB  
Article
A Quantitative Approach to Evaluate the Application of the Extended Situational Teaching Model in Engineering Education
by Fariborz M. Tehrani, Christopher McComb and Sherrianna Scott
Stats 2021, 4(1), 46-61; https://doi.org/10.3390/stats4010004 - 13 Jan 2021
Cited by 5 | Viewed by 3943
Abstract
The extended situational teaching model is a variation of situational teaching, which itself has roots in situational leadership. Application of situational leadership in education requires the teacher to lead students through various stages of the learning process. This paper presents the relationship between [...] Read more.
The extended situational teaching model is a variation of situational teaching, which itself has roots in situational leadership. Application of situational leadership in education requires the teacher to lead students through various stages of the learning process. This paper presents the relationship between performance measures of extended situational teaching and common pedagogical tools in engineering classrooms. These relationships outlined the response of students at different preparation levels to the application of various course components, including classroom activities and out-of-classroom assignments, in respect to task and relationship behaviors. The results of a quantitative survey are presented to support the existence of such a relationship and to demonstrate the effectiveness of the extended situational teaching model. The survey covered 476 engineering students enrolled in nine different courses over a four-year period within the civil engineering program. The statistical analysis of the survey responses proceeded in two stages. The first stage of the analysis evaluates whether the survey tool can resolve meaningful differences between the categories of the situational teaching model, and provides aggregate recommendations for each category. In the second stage of the analysis, the specific instantiation of these categories is broken down according to academic standing (grade point average) and academic level, offering support for an extended situational teaching model. Conclusions discuss the statistical characteristics of the results and correlations between selected pedagogical tools and performance measures. Full article
(This article belongs to the Special Issue Applied Statistics in Engineering)
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15 pages, 2218 KiB  
Article
On the Use of the Cumulative Distribution Function for Large-Scale Tolerance Analyses Applied to Electric Machine Design
by Edmund Marth and Gerd Bramerdorfer
Stats 2020, 3(3), 412-426; https://doi.org/10.3390/stats3030026 - 22 Sep 2020
Cited by 5 | Viewed by 2817
Abstract
In the field of electrical machine design, excellent performance for multiple objectives, like efficiency or torque density, can be reached by using contemporary optimization techniques. Unfortunately, highly optimized designs are prone to be rather sensitive regarding uncertainties in the design parameters. This paper [...] Read more.
In the field of electrical machine design, excellent performance for multiple objectives, like efficiency or torque density, can be reached by using contemporary optimization techniques. Unfortunately, highly optimized designs are prone to be rather sensitive regarding uncertainties in the design parameters. This paper introduces an approach to rate the sensitivity of designs with a large number of tolerance-affected parameters using cumulative distribution functions (CDFs) based on finite element analysis results. The accuracy of the CDFs is estimated using the Dvoretzky–Kiefer–Wolfowitz inequality, as well as the bootstrapping method. The advantage of the presented technique is that computational time can be kept low, even for complex problems. As a demanding test case, the effect of imperfect permanent magnets on the cogging torque of a Vernier machine with 192 tolerance-affected parameters is investigated. Results reveal that for this problem, a reliable statement about the robustness can already be made with 1000 finite element calculations. Full article
(This article belongs to the Special Issue Applied Statistics in Engineering)
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