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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Electro-mechanical Systems01:19

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Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
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Instrument Transformers01:23

Instrument Transformers

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Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
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Linear time-invariant Systems

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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
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Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer

Xanthi Bampoula1, Nikolaos Nikolakis1, Kosmas Alexopoulos1

  • 1Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece.

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Summary

This study introduces a predictive maintenance method using AI to forecast asset failures in manufacturing. The approach combines LSTM-Autoencoders and Transformer encoders for enhanced equipment monitoring and sustainability.

Keywords:
Long Short-Term Memory (LSTM)artificial intelligenceautoencodersdeep learningpredictive maintenanceremaining useful lifetransformers

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Area of Science:

  • Industrial Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Multivariate time-series data enables continuous monitoring of production assets.
  • Modeling these data reveals parameter evolution and interdependencies.
  • AI integration with time-series data offers insights into equipment condition, optimizing resource use and reducing downtime.

Purpose of the Study:

  • To propose a predictive maintenance method for forecasting asset failures.
  • To enhance the sustainability of manufacturing systems through optimized resource utilization and reduced downtime.
  • To develop a remaining useful life (RUL) estimation model for production equipment.

Main Methods:

  • Utilized a combination of Long Short-Term Memory (LSTM)-Autoencoders and a Transformer encoder.
  • Implemented neural networks within a software prototype.
  • Trained and tested models on a dataset from a metal processing industry case study.

Main Results:

  • The developed method enables forecasting of asset failures using spatial and temporal time series.
  • The approach provides insights into equipment condition for predictive maintenance.
  • Successfully trained a remaining useful life (RUL) estimation model.

Conclusions:

  • The proposed AI-driven predictive maintenance method enhances equipment monitoring and sustainability.
  • Combining LSTM-Autoencoders and Transformer encoders is effective for time-series failure forecasting.
  • The developed RUL estimation model contributes to optimizing manufacturing operations.