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Sensors for Predictive Maintenance of Machines
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Sensors for Predictive Maintenance of Machines

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

Deadline for manuscript submissions: 25 June 2025 | Viewed by 7282

Special Issue Editor

Dr. Wenyi Wang

E-Mail Website
Guest Editor
Defence Science and Technology Group (DSTG), Melbourne, VIC 3207, Australia
Interests: signal processing; machine condition monitoring; vibration analysis; AI applications in predictive maintenance

Special Issue Information

Dear Colleagues,

Over the last few years, there have been some major breakthroughs in smart sensing technologies and in artificial intelligence (AI), including the emergence of pre-trained large AI models. Applications of these breakthrough technologies in machine predictive maintenance will have a very significant impact on the operation and maintenance of modern machinery. This Special Issue (SI) is aimed at providing a platform for researchers and developers to share their most current results in machine condition monitoring, structural health monitoring, signal processing for fault detection and diagnosis, smart sensing and edge computing, and non-destructive testing (NDT). Topics of interest include the application of smart sensors and pre-trained large AI models to analyze vibration and acoustic data, text data, image data and voice data for machine predictive maintenance. Contributions can be of both theoretical and application nature and include original research, as well as review and tutorial types of articles. We particularly encourage submissions with the keywords listed below. However, the SI will be under the "Fault Diagnosis & Sensors" Section, and all the keywords of the Section are applicable (but not limited to) to this SI.

Dr. Wenyi 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

  • AI-based machine predictive maintenance
  • mechanical fault detection and diagnosis
  • big data analytics for condition/health monitoring
  • failure trending and prognosis
  • structural health monitoring methods, technologies and systems
  • advanced signal processing for condition/health monitoring
  • smart and novel sensors for condition/health monitoring
  • sensor networks and data fusion for fault diagnosis, failure prognosis and NDT

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

Published Papers (4 papers)

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Research

25 pages, 4802 KiB  
Article
A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors
by Ahmad Aminzadeh, Sasan Sattarpanah Karganroudi, Soheil Majidi, Colin Dabompre, Khalil Azaiez, Christopher Mitride and Eric Sénéchal
Sensors 2025, 25(4), 1006; https://doi.org/10.3390/s25041006 - 8 Feb 2025
Cited by 1 | Viewed by 2240
Abstract
Integrating machine learning algorithms leveraged by advanced data acquisition systems is emerging as a pivotal approach in predictive maintenance. This paper presents the deployment of such an integration on an industrial air compressor unit. This research combines updated concepts from the Internet of [...] Read more.
Integrating machine learning algorithms leveraged by advanced data acquisition systems is emerging as a pivotal approach in predictive maintenance. This paper presents the deployment of such an integration on an industrial air compressor unit. This research combines updated concepts from the Internet of Things, machine learning, multi-sensor data collection, structured data mining, and cloud-based data analysis. To this end, temperature, pressure, and flow rate data were acquired from sensors in contact with the compressor. The observed data were sent to the Structured Query Language database. Then, a Linear Regression model was fitted to the training data, and the optimized model was stored for real-time inference. Afterward, structured data were passed through the model, and if the data exceeded the determined threshold, a warning email was sent to an operator. Adopting the Internet of Things enhances surveillance for specialists, decreasing the failure and damage probabilities. The model achieved 98% accuracy in the Mean Squared Error metric for our regression model. By analyzing the gathered data, the implemented system demonstrates the capabilities to predict potential equipment failures with promising accuracy, facilitating a shift from reactive to proactive maintenance strategies. The findings reveal substantial potential for improvements in maintenance efficiency, equipment uptime, and cost savings. Full article
(This article belongs to the Special Issue Sensors for Predictive Maintenance of Machines)
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24 pages, 7068 KiB  
Article
Bayesian Architecture for Predictive Monitoring of Unbalance Faults in a Turbine Rotor–Bearing System
by Banalata Bera, Shyh-Chin Huang, Po Ting Lin, Yu-Jen Chiu and Jin-Wei Liang
Sensors 2024, 24(24), 8123; https://doi.org/10.3390/s24248123 - 19 Dec 2024
Viewed by 841
Abstract
Unbalance faults are among the common causes of interruptions and unexpected failures in rotary systems. Therefore, monitoring unbalance faults is essential for predictive maintenance. While conventional time-invariant mathematical models can assess the impact of these faults, they often rely on proper assumptions of [...] Read more.
Unbalance faults are among the common causes of interruptions and unexpected failures in rotary systems. Therefore, monitoring unbalance faults is essential for predictive maintenance. While conventional time-invariant mathematical models can assess the impact of these faults, they often rely on proper assumptions of system factors like bearing stiffness and damping characteristics. In reality, continuous high-speed operation and environmental factors like load variations cause these parameters to change. This work presents a novel architecture for unbalance fault monitoring and prognosis, in which the bearing parameters are treated as variables that change with operating conditions. This enables the development of a more reliable mathematical model for continuous monitoring and prognosis of unbalance faults in rotor systems. This Bayesian inference framework uses Markov Chain Monte Carlo (MCMC) sampling to identify dynamic bearing parameters. Specifically, the Metropolis algorithm is employed to systematically evaluate the range of acceptable parameter values within the framework. A novel dual-MCMC loops explore and assess the parameter space, resulting in more accurate and reliable bearing parameter estimations. These updated parameters improve the demonstrated turbine rotor–bearing system’s unbalance assessment up to 74.48% of the residual error compared to models with fixed parameters. This validates the Bayesian framework for predictive monitoring and maintenance-oriented solutions. Full article
(This article belongs to the Special Issue Sensors for Predictive Maintenance of Machines)
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17 pages, 4327 KiB  
Article
A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines
by Md. Shamsul Arifin, Wilson Wang and Mohammad Nasir Uddin
Sensors 2024, 24(16), 5186; https://doi.org/10.3390/s24165186 - 11 Aug 2024
Cited by 1 | Viewed by 1246
Abstract
Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar [...] Read more.
Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally. Full article
(This article belongs to the Special Issue Sensors for Predictive Maintenance of Machines)
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16 pages, 8042 KiB  
Article
Anomaly Detection and Remaining Useful Life Estimation for the Health and Usage Monitoring Systems 2023 Data Challenge
by Omri Matania, Eric Bechhoefer, David Blunt, Wenyi Wang and Jacob Bortman
Sensors 2024, 24(13), 4258; https://doi.org/10.3390/s24134258 - 30 Jun 2024
Cited by 2 | Viewed by 2026
Abstract
Gear fault detection and remaining useful life estimation are important tasks for monitoring the health of rotating machinery. In this study, a new benchmark for endurance gear vibration signals is presented and made publicly available. The new dataset was used in the HUMS [...] Read more.
Gear fault detection and remaining useful life estimation are important tasks for monitoring the health of rotating machinery. In this study, a new benchmark for endurance gear vibration signals is presented and made publicly available. The new dataset was used in the HUMS 2023 conference data challenge to test anomaly detection algorithms. A survey of the suggested techniques is provided, demonstrating that traditional signal processing techniques interestingly outperform deep learning algorithms in this case. Of the 11 participating groups, only those that used traditional approaches achieved good results on most of the channels. Additionally, we introduce a signal processing anomaly detection algorithm and meticulously compare it to a standard deep learning anomaly detection algorithm using data from the HUMS 2023 challenge and simulated signals. The signal processing algorithm surpasses the deep learning algorithm on all tested channels and also on simulated data where there is an abundance of training data. Finally, we present a new digital twin that enables the estimation of the remaining useful life of the tested gear from the HUMS 2023 challenge. Full article
(This article belongs to the Special Issue Sensors for Predictive Maintenance of Machines)
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