Wireless Ammeter Based on ZigBee for Continuous Monitoring of Induction Motors

Gutiérrez, Sebastián; Islas, Laura; Ibanez, Federico Martin; Cardona, Manuel; Calzada, Jorge I.; Solanki, Vijender Kumar

doi:10.1109/concapanxxxix47272.2019.8977048

Cited by 3 publications

(3 citation statements)

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“…Thus, predictive maintenance can be done noninvasively, using temperature and current sensors and computer simulation for digital twins of thermomagnetic behavior [65]. Because induction motors are important parts of equipment in many industries, for their monitoring, an industrial wireless ammeter based on Zigbee technology is proposed as a tool for maintenance, and to identify when the IMs work unusually and to prevent failures [66]. This technology allows a wireless sensor network with remote measurements of currents to monitor the power supply.…”

Section: Are Selected Cases Of Industrial Applications Of Iot-based D...mentioning

confidence: 99%

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Internet of Things-Based Control of Induction Machines: Specifics of Electric Drives and Wind Energy Conversion Systems

Ioannides,

Stamelos,

Papazis

et al. 2024

Energies

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The Internet of Things (IoT) is introduced in systems with electrical machines, such as in electric drive systems, wind energy generating systems, and small and special machines, to remote monitor and control the operation for data acquisition and analysis. These systems can integrate with the equipment and retrofit the existing installations. At the end of the control loops there are always motors, or actuators, of big or small ratings, of rotating or linear movements, electrical or nonelectrical, which must produce the motion. This article analyses selected aspects of research and applications of IoT-based control in electric drive systems and of wind energy conversion systems with induction machines. Various applications and study cases of control systems of electrical machines with IoT technology are described. With the IoT-based control of induction machine systems operators can remotely monitor parameters and obtain accurate real-time feedback during fast changing duty cycle operation. Thus, IoT creates multipurpose instruments in the remote control of induction machines. The paper offers a comprehensive analysis of IoT-based control applications in the field of induction machines, with technical details of design, construction, experimental testing, and prototyping, that are useful to energy engineering specialists in the sector of electric drives and wind energy conversion systems.

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Section: Are Selected Cases Of Industrial Applications Of Iot-based D...mentioning

confidence: 99%

“…• Many layouts have Arduino microcontrollers, of different models and versions, in [24,39,40,56,57,60,62,66,69,73,75,82].…”

mentioning

confidence: 99%

Internet of Things-Based Control of Induction Machines: Specifics of Electric Drives and Wind Energy Conversion Systems

Ioannides,

Stamelos,

Papazis

et al. 2024

Energies

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“…Low-dimensional and hidden features in the raw sensor data are extracted using VAE in the proposed framework, and a loss function is created to extract valuable data features. Gutierrez et al [24] developed an architecture for a wireless ammeter integrated with open source software in order to enhance maintenance-related decision-making for manufacturing industries. Motor current is chosen as the parameter to be analyzed as anomalies in current is one of the biggest indicators of common induction motor issues such as overload and faulty rotor.…”

Section: Introductionmentioning

confidence: 99%

Predictive maintenance of rotational machinery using deep learning

Ali,

Lai,

Abdulla

2024

IJECE

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This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability.

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