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IoT-Embedded Electrical Drives and Machineries: The New Frontier of Edge...
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IoT-Embedded Electrical Drives and Machineries: The New Frontier of Edge Computing and Intelligent Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 4540

Special Issue Editors

Prof. Giuseppe Leonardo Cascella

E-Mail Website
Guest Editor
Polytechnic University of Bari, Italy
Interests: IoT; artificial intelligence; advanced control theory; system identification; energy efficiency; optimization methods; electrical machines; electrical drives; energy management system; power converters
Dr. Elia Brescia

E-Mail Website
Guest Editor
Polytechnic University of Bari, Italy
Interests: electrical machines; electrical drives; wind turbines; finite element analysis; automated design; permanent magnet synchronous machines; parameter identification

Special Issue Information

Dear colleagues,

Cloud computing and cyberphysical systems (CPSs) are increasing the demand for IoT-oriented and edge computing-enabled equipment in order to feed cloud data lakes with more advanced and processed information. Electrical drives and equipment are evolving in smart sensors able to perform their primary operations and simultaneously feed extended cloud data analytics processes. In this transformation, a key role is played by signal conditioning from sensors, filtering, control algorithms, and processing methods that need to be implemented onboard (edge computing) because of real-time computing, bandwidth, etc. In fact, these algorithms perform the first stage of cloud data analytics whose aims are information extraction, model identification, predictions, etc. Finally, electrical drives and equipment with edge-computing techniques can extend their role from manufacturing elements to more powerful virtual agents/sensors in the cloud computing environment.

Therefore, the main themes promoted by this Special Issue are:

  • IoT-oriented electrical drives and equipment
  • Signal conditioning and parameter identification for edge computing applications
  • Edge and cloud computing solutions for industry 4.0 applications (efficiency monitoring, predictive maintenance, zero-defect manufacturing, etc.)
  • AI-based techniques to improve edge computing in electrical drives and equipment
  • Smart and virtual sensors for IoT-embedded systems
  • Quality and accuracy estimation of edge computing
  • Sensor technologies and protocols for IoT systems
  • Advanced strategies for big data, sensor data fusion, and data analytics
  • Edge, fog, and cloud computing architectures for IoT systems
  • Deep integration of data for innovative IoT applications
  • Distributed data processing for IoT
  • Distributed AI algorithms

Prof. Giuseppe Leonardo Cascella
Dr. Elia Brescia
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. Sensors 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 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.

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Published Papers (2 papers)

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Research

24 pages, 6322 KiB  
Article
Cyber–Physical Distributed Intelligent Motor Fault Detection
by Adnan Al-Anbuky, Saud Altaf and Alireza Gheitasi
Sensors 2024, 24(15), 5012; https://doi.org/10.3390/s24155012 - 2 Aug 2024
Viewed by 601
Abstract
This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results [...] Read more.
This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber–physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber–physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system’s performance is evaluated in terms of accuracy, sensitivity, and false positive rates. Full article
(This article belongs to the Special Issue IoT-Embedded Electrical Drives and Machineries: The New Frontier of Edge Computing and Intelligent Sensors)
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25 pages, 4240 KiB  
Article
Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources
by Elia Brescia, Donatello Costantino, Federico Marzo, Paolo Roberto Massenio, Giuseppe Leonardo Cascella and David Naso
Sensors 2021, 21(14), 4699; https://doi.org/10.3390/s21144699 - 9 Jul 2021
Cited by 17 | Viewed by 3019
Abstract
Parameter identification of permanent magnet synchronous machines (PMSMs) represents a well-established research area. However, parameter estimation of multiple running machines in large-scale applications has not yet been investigated. In this context, a flexible and automated approach is required to minimize complexity, costs, and [...] Read more.
Parameter identification of permanent magnet synchronous machines (PMSMs) represents a well-established research area. However, parameter estimation of multiple running machines in large-scale applications has not yet been investigated. In this context, a flexible and automated approach is required to minimize complexity, costs, and human interventions without requiring machine information. This paper proposes a novel identification strategy for surface PMSMs (SPMSMs), highly suitable for large-scale systems. A novel multistep approach using measurement data at different operating conditions of the SPMSM is proposed to perform the parameter identification without requiring signal injection, extra sensors, machine information, and human interventions. Thus, the proposed method overcomes numerous issues of the existing parameter identification schemes. An IoT/cloud architecture is designed to implement the proposed multistep procedure and massively perform SPMSM parameter identifications. Finally, hardware-in-the-loop results show the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue IoT-Embedded Electrical Drives and Machineries: The New Frontier of Edge Computing and Intelligent Sensors)
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