Induction Motor Drive with Trinary DC Source Asymmetrical Inverter

Vijayakumar, A.; Prabaharan, N.

doi:10.35940/ijrte.b2527.078219

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Autoencoders on the other hand are neural networks that are applied for unsupervised learning. They can be used to efficiently learn data patterns and ignore the noise [17]. Autoencoders can be used to check whether data deviates from the regular pattern.…”

Section: Lstm Autoencoder Modelmentioning

confidence: 99%

Anomaly Detection System for Stepper Motors

Sharrar¹

2022

IJERECE

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Predictive maintenance (PdM) systems have the potential to autonomously detect underlying motor issues at early stages. Although many such systems have been proposed up to data, they have yet to be implemented. Most of these methods, which are based on supervised learning, require hours of manual data collection and annotation. Furthermore, they are mostly made to tackle a single instead of the multiple motor issues that may occur and are unable to adapt to varying motor speed and load conditions. Thus, they are not viable for industrial implementation. Therefore, this paper presents an unsupervised LSTM autoencoder-based anomaly detection system for electric motors. It analyzes the vibration and current consumption data from motors to detect anomalies, which is sufficient to account for the various motor defects. The system comes with a variety of features that allows users to autonomously collect data, train models and deploy models. In addition to that, users can remotely keep track of the motor’s conditions. To test the system, a hardware test bench using a stepper motor is made to simulate defective conditions. The LSTM Autoencoder-based anomaly detection system is described step-by-step in this paper.

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Section: Lstm Autoencoder Modelmentioning

confidence: 99%

Anomaly Detection System for Stepper Motors

Sharrar¹

2022

IJERECE

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“…Then the controller does the appropriate action according to the instruction from the App. This process continues until the controller gets power supply as shown in figure 3 [12][13][14][15].…”

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confidence: 99%

IoT Based Smart Control of Load for Demand Side Management

Sankar

Reddy²,

Nagaraja³

et al. 2022

IJEER

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One of the most significant gifts that science has bestowed upon humanity is electricity. It has also assimilated into contemporary life, and it is impossible to imagine existence without it. In our daily lives, electricity serves a variety of purposes. Based on World Bank report, the quantity of lost earnings due to electrical outages is projected at 5.47%. Especially in India, these losses are even more. Energy meter in India doesn't provide two-way communication. However, domestic consumers and farmers cannot control their loads remotely. Another challenge in today’s system is electricity consumers are unaware of their electricity consumption patterns and tariff accounting process. Every time a consumer cannot go outside of the house and check their reading in energy meter. To overcome these challenges, IoT technology has been used. IoT is a new technology that connects users to physical devices. The proposed work uses ESP32 based hardware circuits to control the 220V AC supply power outlet. The main aim of this paper is to develop a sophisticated system that allow remote control of load, real-time monitoring, creating awareness in customers and ultimately to save energy.

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Design and vibration analysis on EMS by using Block Lanczos method for humanoid robotics arm applications

Vijayakumar¹,

Prabhu²

2022

Int J Interact Des Manuf

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Induction Motor Drive with Trinary DC Source Asymmetrical Inverter

Cited by 5 publications

References 15 publications

Anomaly Detection System for Stepper Motors

Anomaly Detection System for Stepper Motors

IoT Based Smart Control of Load for Demand Side Management

Design and vibration analysis on EMS by using Block Lanczos method for humanoid robotics arm applications

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