On the use of Wireless Sensor Networks in Preventative Maintenance for Industry 4.0

Catenazzo, Donato; OrFlynn, Brendan; Walsh, Michael

doi:10.1109/icsenst.2018.8603669

Cited by 25 publications

(15 citation statements)

References 41 publications

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“…Moreover, wireless communications used with WSNs make it easy to deploy and configure PdM systems. Still, it may suffer from some drawbacks: limited energy resources, security, bandwidth, and limited processing capacity [4].…”

Section: Introductionmentioning

confidence: 99%

A predictive maintenance system for wireless sensor networks: a machine learning approach

Almazaideh

Levendovszky

2022

IJEECS

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<p>Predictive maintenance system (PdM) is a new concept that helps system operators evaluate the current status of their systems, and it also assists in predicting the future quality of these systems and scheduling maintenance action. This paper proposes a PdM model that utilizes machine learning to predict the system’s operational status after M active steps based on L previous observations implemented by a feedforward neural network (FFNN). We use quantization and encoding schemes to reduce the complexity of the system. We apply the proposed model to build a PdM system for wireless sensors networks (WSNs), where our concern is to predict the state of the system as far as the quality of data transfer is concerned. The FFNN provides a forward prediction of the operational status of the network after M consecutive time steps in the future, based on the previous L readings of quality of service (QoS) requirements of WSN. We also demonstrate the relation between complexity and accuracy. We found that larger M leads to higher complexity and larger prediction error, where larger L entails higher complexity and smaller prediction error. We also investigate how quantization and encoding can reduce complexity to implement a real-time PdM system.</p>

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Section: Introductionmentioning

confidence: 99%

A predictive maintenance system for wireless sensor networks: a machine learning approach

Almazaideh

Levendovszky

2022

IJEECS

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“…Indeed, due to their high installation costs, wired systems are generally available only for long-term monitoring of important key structures. SHM wireless sensors enable significant cost reductions [6], allowing a massive utilization in public and private infrastructures. Moreover, such sensors are also suitable for short-term structural monitoring applications, exploiting single clusters of devices for multiple buildings.…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring system with Narrowband IoT and MEMS sensors

Brunelli¹,

Nuzzo²,

Polonelli³

et al. 2021

Preprint

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We propose a low-cost wireless sensor node specifically designed to support modal analysis over extended periods of time with long-range connectivity at low power consumption. Our design uses very cost-effective MEMS accelerometers and exploits the Narrowband IoT protocol (NB-IoT) to establish long-distance connection with 4G infrastructure networks. Long-range wireless connectivity, cabling-free installation and multi-year lifetime are a unique combination of features, not available, to the best of our knowledge, in any commercial or research device. We discuss in detail the hardware architecture and power management of the node. Experimental tests demonstrate a lifetime of more than ten years with a 17000 mAh battery or completely energy-neutral operation with a small solar panel (60 mm x 120 mm). Further, we validate measurement accuracy and confirm the feasibility of modal analysis with the MEMS sensors: compared with a high-precision instrument based on a piezoelectric transducer, our sensor node achieves a maximum difference of 0.08% at a small fraction of the cost and power consumption. <br> <br>

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“…Accelerometers are currently among the most widely studied MEMS sensors in a wide range of applications: medical, sports and recreation, entertainment, industrial, etc [2]. This is due to their accuracy in the detection of vibrations and human body or machine movements, their small size, and reasonable power consumption [4,5,6]. Recent works have described the use of accelerometers in specific applications of wireless sensor networks for motor analysis and machine tool performance.…”

Section: Introductionmentioning

confidence: 99%

SmarTEG: An Autonomous Wireless Sensor Node for High Accuracy Accelerometer-Based Monitoring

Magno

Sigrist

Gómez

et al. 2019

Sensors

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We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade’s wear detection, and sends the report wirelessly. Experimental results in a real-world deployment scenario demonstrate that its accuracy is comparable to state-of-the-art algorithms executed on a PC and show the energy-neutrality of the solution using a small thermoelectric generator to harvest energy. The impact of various low-power techniques implemented on the node is analyzed, highlighting the benefits of onboard processing, the nano-power wake-up radio, and the combination of harvesting and low power design. Finally, accurate in-field energy intake measurements, coupled with simulations, demonstrate that the proposed approach is energy autonomous and can work perpetually.

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On the use of Wireless Sensor Networks in Preventative Maintenance for Industry 4.0

Cited by 25 publications

References 41 publications

A predictive maintenance system for wireless sensor networks: a machine learning approach

A predictive maintenance system for wireless sensor networks: a machine learning approach

Structural Health Monitoring system with Narrowband IoT and MEMS sensors

SmarTEG: An Autonomous Wireless Sensor Node for High Accuracy Accelerometer-Based Monitoring

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