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Micromachines
Special Issues
Microdevices, Systems and Algorithms for Assets Management
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Microdevices, Systems and Algorithms for Assets Management

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A：Physics".

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 6962

Special Issue Editors

Prof. Dr. Marc Desmulliez

E-Mail Website
Guest Editor
School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
Interests: built-in self testing; built-in self repair; microsensors; machine learning; microsystems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. David Flynn

E-Mail Website
Guest Editor
School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
Interests: prognostics and health management; energy systems; data analysis; robotics; artificial intelligence

Special Issue Information

Dear Colleagues,

Global society depends on continuity of service from critical systems, which deliver vital services such as energy, transportation, telecommunications, food and water, the built environment, and healthcare. There is also a trend of decarbonisation across these services, thereby establishing increasingly complex interdependencies, interacting at a global scale to create a susceptiblity to catastrophic and cascading failure under stress. Furthermore, assets within these complex systems and networks not only require accurate lifecycle management, they have to increasingly consider implications for second life use and the circular economy.

Intelligent asset management requires the ability to sense, understand, decide upon, and interact with assets. Hence, research focused on the complex industrial applications of alogrithms, sensor systems, and microdevices, for intelligent asset management, are sollicated for this Special Issue.  

This Special Issue will address the strategies; hardware infrastructure, including microsensors; and algorithms needed to implement the intelligent asset management of larger systems. Articles covering one or more of these challenges are welcome that indicate their range of applications.

Prof. Dr. Marc Desmulliez
Prof. Dr. David Flynn
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. Micromachines is an international peer-reviewed open access monthly 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 2600 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

  • Asset management
  • Data analysis
  • Prognostics
  • Artificial intelligence
  • Complex systems
  • Infrastructure
  • Sensor networks
  • Sensing technologies
  • Digital technologies
  • Microsensors
  • Microdevices
  • Machine learning

Published Papers (2 papers)

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Research

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16 pages, 3071 KiB  
Article
A Novel High-Speed and High-Accuracy Mathematical Modeling Method of Complex MEMS Resonator Structures Based on the Multilayer Perceptron Neural Network
by Qingsong Li, Kuo Lu, Kai Wu, Hao Zhang, Xiaopeng Sun, Xuezhong Wu and Dingbang Xiao
Micromachines 2021, 12(11), 1313; https://doi.org/10.3390/mi12111313 - 26 Oct 2021
Cited by 9 | Viewed by 1911
Abstract
MEMS resonators have become core devices in a large number of fields; however, due to their complex structures, the finite element analysis (FEA) method is still the main method for their theoretical analysis. The traditional finite element analysis method faces the disadvantages of [...] Read more.
MEMS resonators have become core devices in a large number of fields; however, due to their complex structures, the finite element analysis (FEA) method is still the main method for their theoretical analysis. The traditional finite element analysis method faces the disadvantages of large calculation amount and long simulation time, which limits the development of high-performance MEMS resonators. This paper demonstrates a high-speed and high-accuracy simulation tool based on the artificial neural network, where a multilayer perceptron (MLP) neural network model is constructed. The typical structural parameters of MEMS resonator are used as the input layer, and its performance indicators produced by the finite element analysis method are the output layer. After iteratively trained with 4000 samples, the cumulative error of the neural network decreases to 0.0017 and a prediction network model is obtained. Compared with the finite element analysis results, the structural accuracy error predicted by the neural network model can be controlled within 6%, but its runtime is shortened by 15,000 times. This high-speed and high-accuracy mathematical modeling method can effectively improve the analyzing efficiency and provide a promising tool for the design and optimization of different complex MEMS resonators, which exhibit remarkable accuracy and speed. Full article
(This article belongs to the Special Issue Microdevices, Systems and Algorithms for Assets Management)
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Review

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28 pages, 9505 KiB  
Review
Built-In Self-Test (BIST) Methods for MEMS: A Review
by Gergely Hantos, David Flynn and Marc P. Y. Desmulliez
Micromachines 2021, 12(1), 40; https://doi.org/10.3390/mi12010040 - 31 Dec 2020
Cited by 16 | Viewed by 4292
Abstract
A novel taxonomy of built-in self-test (BIST) methods is presented for the testing of micro-electro-mechanical systems (MEMS). With MEMS testing representing 50% of the total costs of the end product, BIST solutions that are cost-effective, non-intrusive and able to operate non-intrusively during system [...] Read more.
A novel taxonomy of built-in self-test (BIST) methods is presented for the testing of micro-electro-mechanical systems (MEMS). With MEMS testing representing 50% of the total costs of the end product, BIST solutions that are cost-effective, non-intrusive and able to operate non-intrusively during system operation are being actively sought after. After an extensive review of the various testing methods, a classification table is provided that benchmarks such methods according to four performance metrics: ease of implementation, usefulness, test duration and power consumption. The performance table provides also the domain of application of the method that includes field test, power-on test or assembly phase test. Although BIST methods are application dependent, the use of the inherent multi-modal sensing capability of most sensors offers interesting prospects for effective BIST, as well as built-in self-repair (BISR). Full article
(This article belongs to the Special Issue Microdevices, Systems and Algorithms for Assets Management)
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