Application of Wireless Sensor Networks Technology for Induction Motor Monitoring in Industrial Environments

Gomes, Ruan Delgado; Adissi, Marcéu Oliveira; Silva, Tássio A. B. da; Filho, Abel Cavalcante Lima; Spohn, Marco Aurélio; Belo, Francisco Antônio

doi:10.1007/978-3-319-12892-4_10

Cited by 3 publications

(2 citation statements)

References 81 publications

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“…However, to control the machine for continuous action in production become dangerous and dangerous steps. In this case, remote control and surveillance technology becomes a comparable solution to eliminate these risks [6]. Therefore, wireless data communication has been used in various industries.…”

Section: Previous Research Workmentioning

confidence: 99%

Resilient Model Predictive Control (RMPC) Technique Based Induction Motor Monitoring and Control using Labview

Kavitha,

Vijayalakshmi,

Senthilvadivu

et al. 2020

IJRTE

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Nowadays induction motor is the most popular type of motor for industrial applications. The main advantage of the induction motor is its straightforward rotor construction leading to low cost, hard work, and low maintenance requirements. This work presents a remote control and monitoring the electrical and mechanical faults of an induction motor based on Labview for safe and economic data communication in industrial fields. In this work, the utilization of the Resilient Model Predictive Control (RMPC) to tackle the electrical and mechanical issue in an induction motor is proposed. This strategy allows for synchronization and active fault detection for the important and exclusion of a particular setting. Different importance levels of different control models of performance are evaluated. We continuously monitor technical motor parameters such as the sensor's peak timing, increment duration, peak overflow, and the load current and voltage relaxation error in an induction motor. The measured values are then sent to the processing unit, which displays the processing and displaying parameters that the Gateway module communicates with the Gateway module to send information to the remote monitoring cloud. The system also presents automatic and manual control methods to stop or start the induction motor using Lab view to avoid system failure. The applicability of the proposed framework is to use MATLAB in the creation of simulation results and experiments to approve MATLAB2017a programming and its execution evaluation results show that under actual operating conditions.

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Section: Previous Research Workmentioning

confidence: 99%

Resilient Model Predictive Control (RMPC) Technique Based Induction Motor Monitoring and Control using Labview

Kavitha,

Vijayalakshmi,

Senthilvadivu

et al. 2020

IJRTE

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show abstract

“…In general, sensor nodes are reliable, accurate, flexible, inexpensive, and easy to deploy. Some areas and industries that are subject to environmental constraints rely on WSNs for data collection and monitoring [1]. They are widely used in many applications such as emergency response [2], healthcare monitoring [3], military, agriculture [4], environmental monitoring, and smart power grid [5].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Dynamic Trust Evaluation Model for Wireless Sensor Networks

Wen

Liu

et al. 2017

Journal of Sensors

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Trust evaluation is an effective method to detect malicious nodes and ensure security in wireless sensor networks (WSNs). In this paper, an efficient dynamic trust evaluation model (DTEM) for WSNs is proposed, which implements accurate, efficient, and dynamic trust evaluation by dynamically adjusting the weights of direct trust and indirect trust and the parameters of the update mechanism. To achieve accurate trust evaluation, the direct trust is calculated considering multitrust including communication trust, data trust, and energy trust with the punishment factor and regulating function. The indirect trust is evaluated conditionally by the trusted recommendations from a third party. Moreover, the integrated trust is measured by assigning dynamic weights for direct trust and indirect trust and combining them. Finally, we propose an update mechanism by a sliding window based on induced ordered weighted averaging operator to enhance flexibility. We can dynamically adapt the parameters and the interactive history windows number according to the actual needs of the network to realize dynamic update of direct trust value. Simulation results indicate that the proposed dynamic trust model is an efficient dynamic and attack-resistant trust evaluation model. Compared with existing approaches, the proposed dynamic trust model performs better in defending multiple malicious attacks.

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IoT-Based Control, Monitoring, and Protection System for 3-Phase Induction Motors in Electric Motorcycles

Musyafiq,

Akbar,

Purwiyanto

et al. 2024

PEC

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This study investigates the application of the Internet of Things (IoT) for wireless control and monitoring of a 3-phase electric motor using a smartphone. The system integrates PZEM-004T, DS18B20, and Hall Effect sensors to collect data on voltage, current, temperature, and rotational speed using the NodeMCU ESP8266 microcontroller. Measurements are displayed on an LCD and transmitted to the Blynk server for smartphone access. A comparative method evaluates the accuracy of sensor readings against standard measuring instruments. Results obtained an average percentage error of 0.5% for the R phase voltage, 0.2% for the S phase, and 0.1% for the T phase. Current measurements reveal errors of 5% for the R phase, 10.3% for the S phase, and 11.7% for the T phase. The control system’s performance varies with internet speed, with an average delay of 0.99 seconds on a 4G network and 2.51 seconds on 3G. Additionally, the study evaluates three protection mechanisms, demonstrating that the motor stops within 4.03 seconds in the event of a phase failure, while overcurrent and overheating protections activate within 8.47 seconds and 3.64 seconds, respectively. Overall, the findings affirm the viability of IoT in motor monitoring and control, emphasizing accuracy and response times under varying conditions.

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Application of Wireless Sensor Networks Technology for Induction Motor Monitoring in Industrial Environments

Cited by 3 publications

References 81 publications

Resilient Model Predictive Control (RMPC) Technique Based Induction Motor Monitoring and Control using Labview

Resilient Model Predictive Control (RMPC) Technique Based Induction Motor Monitoring and Control using Labview

An Efficient Dynamic Trust Evaluation Model for Wireless Sensor Networks

IoT-Based Control, Monitoring, and Protection System for 3-Phase Induction Motors in Electric Motorcycles

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