Real time wireless condition monitoring of induction motor

Sridhar, Seetharaman; Rao, K. Uma; Nihaal, M. S.; Aashik, A. K. Shetty

doi:10.1109/ieacon.2016.8067375

Cited by 4 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system was evaluated on an experimental test bench. Sridhar et al [26] describes a system for fault classification of induction motors by processing stator current measurements using neural networks.…”

Section: Iot-based Induction Motor Monitoringmentioning

confidence: 99%

Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning

Sunal,

Velisavljevic,

Dyo

et al. 2024

Sensors

View full text Add to dashboard Cite

The centrifugal pump is the workhorse of many industrial and domestic applications, such as water supply, wastewater treatment and heating. While modern pumps are reliable, their unexpected failures may jeopardise safety or lead to significant financial losses. Consequently, there is a strong demand for early fault diagnosis, detection and predictive monitoring systems. Most prior work on machine learning-based centrifugal pump fault detection is based on either synthetic data, simulations or data from test rigs in controlled laboratory conditions. In this research, we attempted to detect centrifugal pump faults using data collected from real operational pumps deployed in various places in collaboration with a specialist pump engineering company. The detection was done by the binary classification of visual features of DQ/Concordia patterns with residual networks. Besides using a real dataset, this study employed transfer learning from the image detection domain to systematically solve a real-life problem in the engineering domain. By feeding DQ image data into a popular and high-performance residual network (e.g., ResNet-34), the proposed approach achieved up to 85.51% classification accuracy.

show abstract

Section: Iot-based Induction Motor Monitoringmentioning

confidence: 99%

Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning

Sunal,

Velisavljevic,

Dyo

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…2. These types of faults are mainly caused by one or a combination of the following reasons [4][5][6]:…”

Section: Introductionmentioning

confidence: 99%

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

2022

View full text Add to dashboard Cite

Motors are the higher energy-conversion devices that consume around 40% of the global electrical generated energy. Induction motors are the most popular motor type due to their reliability, robustness, and low cost. Therefore, both condition monitoring and fault diagnosis of induction motor faults have motivated considerable research efforts. In this paper, a comprehensive review of the recent techniques proposed in the literature for broken bar faults detection and diagnosis is presented. This paper mainly investigates the fault detection methods in line-fed and inverter-fed motors proposed after 2015 and published in most relevant journals and conferences. The introduced review has deeply discussed the main features of the reported methods and compared them in many different aspects. Finally, the study has highlighted the main issues and the gaps that require more attention from researchers in this field.

show abstract

“…The bibliographical revision realized for the monitoring of IMs with wireless technologies, has been elaborated on the basis of the measured parameters, wireless technology, sensors and base platform. Table 1 shows the results of the review: [19] v, i Wi-Fi PZEM-004t Arduino Uno R3, Arduino D1R1 [20,21] v, i, T, n ZigBee Not provide Commercial Zigbee board [22] v, i, temperature, n ZigBee ADE7758 Commercial Zigbee board [23,24] i ZigBee ACS 712 Commercial Zigbee board [25] T, n ZigBee Not provide Commercial Zigbee board [26] vibration ZigBee ADXL335(vibration) Commercial Zigbee board [27] v, i, powers, temperature ZigBee Not provide Commercial Zigbee board [28] v, i, n, temperature ZigBee DS18B20(Temperature) Current and voltage transformers Commercial Zigbee board [29] v, i, T, n ZigBee Not provide Cirronet ZMN2400HP [30] v, i ZigBee Not provide Commercial Zigbee board [31] i, vibration ZigBee…”

Section: Introductionmentioning

confidence: 99%

“…Most of them need a Wi-Fi network in the installation [16][17][18][19] with sufficient coverage that involves installing many antennas. ZigBee and Bluetooth have a short range [20][21][22][23][24][25][26][27][28][29][30][31]. LoRa has great coverage and only a gateway to upload data to the cloud.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of the Efficiency and Conditions of Induction Motor Operations by Smart Meter Prototype Based on a LoRa Wireless Network

Caño

Sánchez-Sutil

2019

Electronics

View full text Add to dashboard Cite

The installation of smart meters in the industry to monitor induction motors (IMs) provides easy access to the measurements of the electrical and mechanical variables, which improves the installation process. Using smart meters in industry requires temporary high-resolution data to improve the energy efficiency (EE) and power factor (PF) of IMs. For these purposes, Long Range (LoRa) is an ideal wireless protocol for the usage in industries due to its low energy consumption. In addition, it provides secure communications and long range indoors and outdoors. LoRa avoids the need to install antennas or routers to extend coverage, as each gateway can service 300 LoRa devices with distances of up to 10 km. For this purpose, this research successfully developed a new prototype for a low-cost IM Efficiency Monitoring System using LoRa (IMEMSL). IMEMSL is based on the Arduino open-source electronic platform. It sends the acquired data through the LoRa low-power wide-area-network (LPWAN) and cloud access gateway. This document describes the hardware and software design and implementation. The experimental results are presented and discussed. Received Signal Strength Indicator (RSSI) and time on air measured endorse the use of LoRa for this type of meters. The approximate cost of the prototype was €72 and €93 for the versions without and with Global Positioning System (GPS), respectively.

show abstract

Real time wireless condition monitoring of induction motor

Cited by 4 publications

References 19 publications

Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning

Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

Monitoring of the Efficiency and Conditions of Induction Motor Operations by Smart Meter Prototype Based on a LoRa Wireless Network

Contact Info

Product

Resources

About