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Modeling, Monitoring, Diagnosis, Prognosis and Control in Electromechanical Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (25 March 2023) | Viewed by 8561

Special Issue Editors

Department of Electromechanical Engineering, University of Macau, Macao, China
Interests: intelligent control; dynamics and control; mechanism and machine theory; autonoumous system; fault tolerant control; artificial intelligence with engineering applications; machine learning methods; signal processing; intelligent transportation; system modeling and identification
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Guest Editor
Faculty of Science and Technology, University of Macau, Macau 999078, China
Interests: automotive engines, drive trains and chassis; intelligent automotive systems; artificial intelligence; fluid power engineering; mechanical vibration; manufacturing technology for biomedical applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modeling, monitoring, diagnostic, prognostic and automatic control systems are commonly found in many engineering domains, including mechanical engineering, electrical engineering, control engineering, civil engineering and biomedical engineering, etc. Intelligent systems have the capability to acquire and apply knowledge in an intelligent manner and use perception, reasoning and learning to make decisions based on incomplete information. Therefore, intelligent system approaches to modeling, monitoring, diagnosis, prognosis and control systems can pave a practical path for a variety of engineering applications in the absence of human interactions.

The aim of this Special Issue is to create an international forum for scientists and practicing engineers throughout the world to publish their latest research findings and ideas regarding modeling, intelligent monitoring, diagnostic, prognostic and control systems for engineering applications. This Special Issue welcomes theoretical contributions that aim to advance our understanding of intelligent techniques, including neurocomputing, deep learning, fuzzy logic, evolutionary algorithms, swarm intelligence and interdisciplinary topics. Moreover, this Special Issue also welcomes reports on innovative engineering applications that focus on the modeling, monitoring, diagnosis, prognosis and control of physical processes or systems with no (or very little) human interactions.

This Special Issue will collate original research articles as well as review articles. Potential topics include, but are not limited to:

  • Modeling methodology of complex electromechanical systems;
  • Fault detection;
  • Image- and signal-based diagnosis;
  • Prognosis of remaining useful life of core equipment;
  • System modeling, identification and prediction;
  • Intelligent control and automation;
  • Process control, motion control, force control, vibration control and fault-tolerant control;
  • Fuzzy logic systems;
  • Swarm intelligence and evolutionary algorithms;
  • Machine learning methods;
  • Signal processing and pattern recognition;
  • Hybrid algorithms in intelligent transportation;
  • The control of intelligent and unmanned systems.

Dr. Jing Zhao
Prof. Dr. Pak Kin Wong
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.

Keywords

  • electromechanical systems
  • modeling and identification
  • advanced sensing systems
  • the control of intelligent systems
  • fault-tolerant control
  • the modeling and control of robotics

Published Papers (4 papers)

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Research

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17 pages, 11938 KiB  
Article
Fault Diagnosis Method of Roadheader Bearing Based on VMD and Domain Adaptive Transfer Learning
by Xiaofei Qu and Yongkang Zhang
Sensors 2023, 23(11), 5134; https://doi.org/10.3390/s23115134 - 28 May 2023
Viewed by 1221
Abstract
The roadheader is a core piece of equipment for underground mining. The roadheader bearing, as its key component, often works under complex working conditions and bears large radial and axial forces. Its health is critical to efficient and safe underground operation. The early [...] Read more.
The roadheader is a core piece of equipment for underground mining. The roadheader bearing, as its key component, often works under complex working conditions and bears large radial and axial forces. Its health is critical to efficient and safe underground operation. The early failure of a roadheader bearing has weak impact characteristics and is often submerged in complex and strong background noise. Therefore, a fault diagnosis strategy that combines variational mode decomposition and a domain adaptive convolutional neural network is proposed in this paper. To start with, VMD is utilized to decompose the collected vibration signals to obtain the sub-component IMF. Then, the kurtosis index of IMF is calculated, with the maximum index value chosen as the input of the neural network. A deep transfer learning strategy is introduced to solve the problem of the different distributions of vibration data for roadheader bearings under variable working conditions. This method was implemented in the actual bearing fault diagnosis of a roadheader. The experimental results indicate that the method is superior in terms of diagnostic accuracy and has practical engineering application value. Full article
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29 pages, 12405 KiB  
Article
Torque Measurement and Control for Electric-Assisted Bike Considering Different External Load Conditions
by Ping-Jui Ho, Chen-Pei Yi, Yi-Jen Lin, Wei-Der Chung, Po-Huan Chou and Shih-Chin Yang
Sensors 2023, 23(10), 4657; https://doi.org/10.3390/s23104657 - 11 May 2023
Cited by 4 | Viewed by 2970
Abstract
This paper proposes a novel torque measurement and control technique for cycling-assisted electric bikes (E-bikes) considering various external load conditions. For assisted E-bikes, the electromagnetic torque from the permanent magnet (PM) motor can be controlled to reduce the pedaling torque generated by the [...] Read more.
This paper proposes a novel torque measurement and control technique for cycling-assisted electric bikes (E-bikes) considering various external load conditions. For assisted E-bikes, the electromagnetic torque from the permanent magnet (PM) motor can be controlled to reduce the pedaling torque generated by the human rider. However, the overall cycling torque is affected by external loads, including the cyclist’s weight, wind resistance, rolling resistance, and the road slope. With knowledge of these external loads, the motor torque can be adaptively controlled for these riding conditions. In this paper, key E-bike riding parameters are analyzed to find a suitable assisted motor torque. Four different motor torque control methods are proposed to improve the E-bike’s dynamic response with minimal variation in acceleration. It is concluded that the wheel acceleration is important to determine the E-bike’s synergetic torque performance. A comprehensive E-bike simulation environment is developed with MATLAB/Simulink to evaluate these adaptive torque control methods. In this paper, an integrated E-bike sensor hardware system is built to verify the proposed adaptive torque control. Full article
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14 pages, 4657 KiB  
Article
Rational Resampling Ratio as Enhancement to Shaft Imbalance Detection
by Adam Jablonski
Sensors 2023, 23(3), 1719; https://doi.org/10.3390/s23031719 - 03 Feb 2023
Viewed by 1125
Abstract
Trend analysis is one of the most powerful techniques for monitoring the technical condition of individual mechanical components of rotating machinery. It is based on extraction of characteristic signal components according to kinetostatic configuration of the machine drivetrain. It has been used for [...] Read more.
Trend analysis is one of the most powerful techniques for monitoring the technical condition of individual mechanical components of rotating machinery. It is based on extraction of characteristic signal components according to kinetostatic configuration of the machine drivetrain. It has been used for decades and is well-understood. However, classical trend analysis is based on some assumptions which have resulted from the limited computational power of embedded systems years ago. This paper tries to answer a question on whether the assumption of a single signal resampling path for calculation of signal components generated by shafts with rational transmission ratio is valid. The study was conducted using an extensive imbalance test on a medium-power test rig. The paper originally demonstrates that application of an advanced resampling algorithm does not significantly influence the overall trend increase, but it is of utmost importance when trend variance is of interest. Full article
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Review

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35 pages, 786 KiB  
Review
Weakly Hard Real-Time Model for Control Systems: A Survey
by Karla Salamun, Ivan Pavić, Hrvoje Džapo and Ivana Čuljak
Sensors 2023, 23(10), 4652; https://doi.org/10.3390/s23104652 - 11 May 2023
Viewed by 2568
Abstract
The concept of weakly hard real-time systems can be used to model real-time systems that may tolerate occasional deadline misses in a bounded and predictable manner. This model applies to many practical applications and is particularly interesting in the context of real-time control [...] Read more.
The concept of weakly hard real-time systems can be used to model real-time systems that may tolerate occasional deadline misses in a bounded and predictable manner. This model applies to many practical applications and is particularly interesting in the context of real-time control systems. In practice, applying hard real-time constraints may be too rigid since a certain amount of deadline misses is acceptable in some applications. In order to maintain system stability, limitations on the amount and distribution of violated deadlines need to be imposed. These limitations can be formally expressed as weakly hard real-time constraints. Current research in the field of weakly hard real-time task scheduling is focused on designing scheduling algorithms that guarantee the fulfillment of constraints, while aiming to maximize the total number of timely completed task instances. This paper provides an extensive literature review of the work related to the weakly hard real-time system model and its link to the field of control systems design. The weakly hard real-time system model and the corresponding scheduling problem are described. Furthermore, an overview of system models derived from the generalized weakly hard real-time system model is provided, with an emphasis on models that apply to real-time control systems. The state-of-the-art algorithms for scheduling tasks with weakly hard real-time constraints are described and compared. Finally, an overview of controller design methods that rely on the weakly hard real-time model is given. Full article
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