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Smart Sensors for Machine Condition Monitoring and Fault Diagnosis
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Smart Sensors for Machine Condition Monitoring and Fault Diagnosis

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 7317

Special Issue Editor

Prof. Dr. Wilson Q. Wang

E-Mail Website
Guest Editor
Department of Mechanical and Mechatronics Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
Interests: mechatronics; smart sensors; fault diagnosis; system state prognosis; AI; machine learning; system control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of smart sensors in industrial applications has become increasingly important in recent years; they allow us to collect, analyze, and act on data in real-time, so as to improve productivity, efficiency, and safety. This Special Issue aims to provide a platform for researchers and practitioners to share their latest findings, insights, and experiences related to the development, deployment, and use of smart sensors for machine condition monitoring and fault detection.

We invite submissions of original research papers, review articles, and case studies that address the following topics (but are not limited to):

  • Novel smart sensor technologies and their applications in machine condition monitoring
  • Signal processing and data analysis for sensor-based fault detection
  • System state prognosis
  • Machine learning and artificial intelligence techniques for sensor data analysis
  • Robustness and reliability of sensor-based monitoring systems
  • Integration of sensor data with other types of data (e.g., maintenance records, environmental data) for system health management
  • Applications of sensor-based monitoring systems in industrial settings

We welcome contributions from researchers, engineers, and practitioners in academia, industry, and government. All submitted papers will undergo a rigorous peer review process to ensure high quality and relevance to the theme of the Special Issue.

Prof. Dr. Wilson Q. Wang
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. 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

  • smart sensors and instrumentation
  • machine health monitoring
  • fault diagnosis
  • prognosis and system state forecasting
  • signal processing
  • AI and machine learning

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

Published Papers (4 papers)

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Research

36 pages, 16375 KiB  
Article
Fatigue Analysis of a Jacket-Supported Offshore Wind Turbine at Block Island Wind Farm
by Nasim Partovi-Mehr, John DeFrancisci, Mohsen Minaeijavid, Babak Moaveni, Daniel Kuchma, Christopher D. P. Baxter, Eric M. Hines and Aaron S. Bradshaw
Sensors 2024, 24(10), 3009; https://doi.org/10.3390/s24103009 - 9 May 2024
Cited by 1 | Viewed by 1143
Abstract
Offshore wind-turbine (OWT) support structures are subjected to cyclic dynamic loads with variations in loadings from wind and waves as well as the rotation of blades throughout their lifetime. The magnitude and extent of the cyclic loading can create a fatigue limit state [...] Read more.
Offshore wind-turbine (OWT) support structures are subjected to cyclic dynamic loads with variations in loadings from wind and waves as well as the rotation of blades throughout their lifetime. The magnitude and extent of the cyclic loading can create a fatigue limit state controlling the design of support structures. In this paper, the remaining fatigue life of the support structure for a GE Haliade 6 MW fixed-bottom jacket offshore wind turbine within the Block Island Wind Farm (BIWF) is assessed. The fatigue damage to the tower and the jacket support structure using stress time histories at instrumented and non-instrumented locations are processed. Two validated finite-element models are utilized for assessing the stress cycles. The modal expansion method and a simplified approach using static calculations of the responses are employed to estimate the stress at the non-instrumented locations—known as virtual sensors. It is found that the hotspots at the base of the tower have longer service lives than the jacket. The fatigue damage to the jacket leg joints is less than 20% and 40% of its fatigue capacity during the 25-year design lifetime of the BIWF OWT, using the modal expansion method and the simplified static approach, respectively. Full article
(This article belongs to the Special Issue Smart Sensors for Machine Condition Monitoring and Fault Diagnosis)
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16 pages, 4174 KiB  
Article
Smart Sensor-Based Monitoring Technology for Machinery Fault Detection
by Ming Zhang, Xing Xing and Wilson Wang
Sensors 2024, 24(8), 2470; https://doi.org/10.3390/s24082470 - 12 Apr 2024
Cited by 4 | Viewed by 1299
Abstract
Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to [...] Read more.
Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests. Full article
(This article belongs to the Special Issue Smart Sensors for Machine Condition Monitoring and Fault Diagnosis)
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14 pages, 3449 KiB  
Article
Application of Pathfinding Algorithms in Partial Discharge Localization in Power Transformers
by Chandra Prakash Beura, Jorim Wolters and Stefan Tenbohlen
Sensors 2024, 24(2), 685; https://doi.org/10.3390/s24020685 - 21 Jan 2024
Cited by 5 | Viewed by 2279
Abstract
The introduction of artificial intelligence (AI) to ultra-high-frequency (UHF) partial discharge (PD) monitoring systems in power transformers for the localization of PD sources can help create a robust and reliable system with high usability and precision. However, training the AI with experimental data [...] Read more.
The introduction of artificial intelligence (AI) to ultra-high-frequency (UHF) partial discharge (PD) monitoring systems in power transformers for the localization of PD sources can help create a robust and reliable system with high usability and precision. However, training the AI with experimental data or data from electromagnetic simulation is costly and time-consuming. Furthermore, electromagnetic simulations often calculate more data than needed, whereas, for localization, the signal time-of-flight information is the most important. A tailored pathfinding algorithm can bypass the time-consuming and computationally expensive process of simulating or collecting data from experiments and be used to create the necessary training data for an AI-based monitoring system of partial discharges in power transformers. In this contribution, Dijkstra’s algorithm is used with additional line-of-sight propagation algorithms to determine the paths of the electromagnetic waves generated by PD sources in a three-dimensional (3D) computer-aided design (CAD) model of a 300 MVA power transformer. The time-of-flight information is compared with results from experiments and electromagnetic simulations, and it is found that the algorithm maintains accuracy similar to that of the electromagnetic simulation software, with some under/overestimations in specific scenarios, while being much faster at calculations. Full article
(This article belongs to the Special Issue Smart Sensors for Machine Condition Monitoring and Fault Diagnosis)
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16 pages, 7519 KiB  
Article
A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains
by Sitong Sun, Sheng Zhang and Wilson Wang
Sensors 2023, 23(14), 6392; https://doi.org/10.3390/s23146392 - 14 Jul 2023
Viewed by 1807
Abstract
In this work, a new monitoring system is developed for bearing fault detection in high-speed trains. Firstly, a data acquisition system is developed to collect vibration and other related signals wirelessly. Secondly, a new multiple correlation analysis (MCA) technique is proposed for bearing [...] Read more.
In this work, a new monitoring system is developed for bearing fault detection in high-speed trains. Firstly, a data acquisition system is developed to collect vibration and other related signals wirelessly. Secondly, a new multiple correlation analysis (MCA) technique is proposed for bearing fault detection. The MCA technique consists of the three processing steps: (1) the collected vibration signal is decomposed by variational modal decomposition (VMD) to formulate the representative intrinsic mode functions (IMFs); (2) the MCA is used to process and identify the characteristic features for signal analysis; (3) bearing fault is diagnosed by examining bearing characteristic frequency information on the envelope power spectrum. The effectiveness of the proposed MCA fault detection technique is verified by experimental tests corresponding to different bearing conditions. Full article
(This article belongs to the Special Issue Smart Sensors for Machine Condition Monitoring and Fault Diagnosis)
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