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Wireless Sensor Networks for Condition Monitoring
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Wireless Sensor Networks for Condition Monitoring

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 3470

Special Issue Editor

Dr. Juan C. Granda

E-Mail Website
Guest Editor
Departament of Computer Science and Engineering, University of Oviedo, Campus de Gijón, 33203 Asturias, Spain
Interests: industrial IoT; predictive maintenance; wireless sensor networks; renewable energy

Special Issue Information

Dear Colleagues,

Condition monitoring (CM) usually requires continuous monitoring of physical variables such as vibrations, electric current, sound or temperature from running machinery to implement maintenance policies using machine learning models.

Traditionally, continuous monitoring has focused on critical machinery only, but wireless sensor networks (WSNs) enable the deployment of myriads of sensors capable of sensing, computing and communicating wirelessly to gather information from industrial equipment.

The Special Issue on "Wireless Sensor Networks for Condition Monitoring” aims to explore the latest advancements, challenges, and opportunities of WSNs across different sectors when applied to condition monitoring. Contributions from researchers, practitioners, and experts in the field proposing novel methodologies, applications, and best practices in this domain are welcomed.

Dr. Juan C. Granda
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

  • industrial IoT
  • fault detection and diagnosis
  • predictive maintenance
  • sensors for condition monitoring
  • WSNs in harsh environments
  • deep learning or machine learning models for condition monitoring
  • energy-aware condition monitoring
  • cloud, edge, fog and combined cloud-edge computing architectures for condition monitoring
  • case studies and practical implementations of WSNs in monitoring critical infrastructure
  • security and privacy issues in WSN-based condition monitoring systems
  • energy harvesting in WSN

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Published Papers (3 papers)

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Research

21 pages, 3172 KiB  
Article
An Integrated Approach: A Hybrid Machine Learning Model for the Classification of Unscheduled Stoppages in a Mining Crushing Line Employing Principal Component Analysis and Artificial Neural Networks
by Pablo Viveros, Cristian Moya, Rodrigo Mena, Fredy Kristjanpoller and David R. Godoy
Sensors 2024, 24(17), 5804; https://doi.org/10.3390/s24175804 - 6 Sep 2024
Viewed by 711
Abstract
This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type [...] Read more.
This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Condition Monitoring)
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14 pages, 4196 KiB  
Article
Edge Computing and Fault Diagnosis of Rotating Machinery Based on MobileNet in Wireless Sensor Networks for Mechanical Vibration
by Yi Huang, Shuang Liang, Tingqiong Cui, Xiaojing Mu, Tianhong Luo, Shengxue Wang and Guangyong Wu
Sensors 2024, 24(16), 5156; https://doi.org/10.3390/s24165156 - 9 Aug 2024
Viewed by 988
Abstract
With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time [...] Read more.
With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time data processing capabilities. Conventional methods, reliant on experiential approaches, have proven inefficient in meeting these evolving challenges. To this end, a fault detection method for rotating machinery based on mobileNet in MvWSNs is proposed to address these intractable issues. The small and light deep learning model is helpful to realize nearly real-time sensing and fault detection, lightening the communication pressure of MvWSNs. The well-trained deep learning is implanted on the MvWSNs sensor node, an edge computing platform developed via embedded STM32 microcontrollers (STMicroelectronics International NV, Geneva, Switzerland). Data acquisition, data processing, and data classification are all executed on the computing- and energy-constrained sensor node. The experimental results demonstrate that the proposed fault detection method can achieve about 0.99 for the DDS dataset and an accuracy of 0.98 in the MvWSNs sensor node. Furthermore, the final transmission data size is only 0.1% compared to the original data size. It is also a time-saving method that can be accomplished within 135 ms while the raw data will take about 1000 ms to transmit to the monitoring center when there are four sensor nodes in the network. Thus, the proposed edge computing method shows good application prospects in fault detection and control of rotating machinery with high time sensitivity. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Condition Monitoring)
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23 pages, 10092 KiB  
Article
RSSI-WSDE: Wireless Sensing of Dynamic Events Based on RSSI
by Xiaoping Tian, Song Wu, Xiaoyan Zhang, Lei Du and Sencao Fan
Sensors 2024, 24(15), 4952; https://doi.org/10.3390/s24154952 - 31 Jul 2024
Viewed by 916
Abstract
Wireless sensing is a crucial technology for building smart cities, playing a vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless sensing enables the formulation of more scientific decisions. The wireless sensing of [...] Read more.
Wireless sensing is a crucial technology for building smart cities, playing a vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless sensing enables the formulation of more scientific decisions. The wireless sensing of dynamic events is a significant branch of wireless sensing. Sensing the specific times and durations of dynamic events is a challenging problem due to the dynamic event information is concealed within static environments. To effectively sense the relevant information of event occurrence, we propose a wireless sensing method for dynamic events based on RSSI, named RSSI-WSDE. RSSI-WSDE utilizes variable-length sliding windows and statistical methods to process original RSSI time series, amplifying the differences between dynamic events and static environments. Subsequently, z-score normalization is employed to enhance the comparability of the sensing effects for different dynamic events. Furthermore, by setting the adaptive threshold, the occurrence of dynamic event is sensed and the relevant information is marked on the original RSSI time series. In this study, the sensing performance of RSSI-WSDE was tested in indoor corridors and outdoor urban road environments. The wireless sensing of dynamic events, including walking, running, cycling, and driving, was conducted. The experimental results demonstrate that RSSI-WSDE can accurately sense the occurrence of dynamic events, marking the specific time and duration with millisecond-level precision. Moreover, RSSI-WSDE exhibits robust performance in wireless sensing of dynamic events in both indoor and outdoor environments. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Condition Monitoring)
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