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Statistical and Stochastic Approaches for Predictive Maintenance in the Context...
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Statistical and Stochastic Approaches for Predictive Maintenance in the Context of Industry 4.0

A special issue of Mathematical and Computational Applications (ISSN 2297-8747).

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 17379

Special Issue Editor

Prof. Dr. Luca Liliana

E-Mail Website
Guest Editor
Faculty of Engineering and Architecture, University of Enna KORE, Cittadella Universitaria, 94100 Enna, Italy
Interests: statistics; stochastic processes; predictive modeling

Special Issue Information

Dear Colleagues,

Thanks to new digital technologies, it is possible to interconnect, in industrial processes, production machines with their software. 

This technological progress has several advantages, including accelerating digital data collection, optimizing production process times, producing higher quality goods at lower costs, and having all the necessary information to implement strategic decisions to support the business. With the rapid advancement of sensor and network technology, there has been a notable increase in the availability of condition monitoring data such as vibration, temperature, pressure, voltage, and other electrical and mechanical parameters. With the introduction of big data, it is possible to prevent potential failures and estimate the remaining useful life of the equipment by developing advanced mathematical models and Artificial Intelligent (AI) techniques. These approaches allow taking maintenance actions quickly and appropriately. Timely maintenance actions are, in fact, essential for the operation of industrial equipment as they can significantly improve the reliability, availability, and safety of the equipment and minimize failures. This new maintenance paradigm which involves statistical inference approaches and AI techniques is called predictive maintenance and nowadays represents the most promising maintenance strategy. For this reason, it has gradually replaced traditional maintenance strategies such as corrective and preventive maintenance.

Articles related to the development and properties of statistical inference approaches, stochastic methods, and AI techniques for predictive maintenance are welcome in this Special Issue. Papers comparing different statistical and stochastic approaches are particularly welcome. Authors are invited to upload supplementary material, e.g., software, data-sets, or instructive videos complementing the research.

Prof. Dr. Luca Liliana
Guest Editor

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. Mathematical and Computational Applications is an international peer-reviewed open access semimonthly 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 1400 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.

Published Papers (2 papers)

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Research

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20 pages, 646 KiB  
Article
On a Modified Weighted Exponential Distribution with Applications
by Christophe Chesneau, Vijay Kumar, Mukti Khetan and Mohd Arshad
Math. Comput. Appl. 2022, 27(1), 17; https://doi.org/10.3390/mca27010017 - 21 Feb 2022
Cited by 5 | Viewed by 3139
Abstract
Practitioners in all applied domains value simple and adaptable lifetime distributions. They make it possible to create statistical models that are relatively easy to manage. A novel simple lifetime distribution with two parameters is proposed in this article. It is based on a [...] Read more.
Practitioners in all applied domains value simple and adaptable lifetime distributions. They make it possible to create statistical models that are relatively easy to manage. A novel simple lifetime distribution with two parameters is proposed in this article. It is based on a parametric mixture of the exponential and weighted exponential distributions, with a mixture weight depending on a parameter of the involved distribution; no extra parameter is added in this mixture operation. It can also be viewed as a special generalized mixture of two exponential distributions. This decision is based on sound mathematical and physical reasoning; the weight modification allows us to combine some joint properties of the exponential and weighted exponential distribution, which are known as complementary in several modeling aspects. As a result, the proposed distribution may have a decreasing or unimodal probability density function and possess the demanded increasing hazard rate property. Other properties are studied, such as the moments, Bonferroni and Lorenz curves, Rényi entropy, stress-strength reliability, and mean residual life function. Subsequently, a part is devoted to the associated model, which demonstrates how it can be used in a real-world statistical scenario involving data. In this regard, we demonstrate how the estimated model performs well using five different estimation methods and simulated data. The analysis of two data sets demonstrates these excellent results. The new model is compared to the weighted exponential, Weibull, gamma, and generalized exponential models for performance. The obtained comparison results are overwhelmingly in favor of the proposed model according to some standard criteria. Full article
(This article belongs to the Special Issue Statistical and Stochastic Approaches for Predictive Maintenance in the Context of Industry 4.0)
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Review

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21 pages, 1318 KiB  
Review
Predictive Maintenance in the Automotive Sector: A Literature Review
by Fabio Arena, Mario Collotta, Liliana Luca, Marianna Ruggieri and Francesco Gaetano Termine
Math. Comput. Appl. 2022, 27(1), 2; https://doi.org/10.3390/mca27010002 - 31 Dec 2021
Cited by 22 | Viewed by 13670
Abstract
With the rapid advancement of sensor and network technology, there has been a notable increase in the availability of condition-monitoring data such as vibration, temperature, pressure, voltage, and other electrical and mechanical parameters. With the introduction of big data, it is possible to [...] Read more.
With the rapid advancement of sensor and network technology, there has been a notable increase in the availability of condition-monitoring data such as vibration, temperature, pressure, voltage, and other electrical and mechanical parameters. With the introduction of big data, it is possible to prevent potential failures and estimate the remaining useful life of the equipment by developing advanced mathematical models and artificial intelligence (AI) techniques. These approaches allow taking maintenance actions quickly and appropriately. In this scenario, this paper presents a systematic literature review of statistical inference approaches, stochastic methods, and AI techniques for predictive maintenance in the automotive sector. It provides a summary on these approaches, their main results, challenges, and opportunities, and it supports new research works for vehicle predictive maintenance. Full article
(This article belongs to the Special Issue Statistical and Stochastic Approaches for Predictive Maintenance in the Context of Industry 4.0)
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