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Fault Types

Fault Types

When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...

Distributed Loads: Problem Solving

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...

Three-Phase Short Circuit—Unloaded Synchronous Machine

Three-Phase Short Circuit—Unloaded Synchronous Machine

Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...

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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Published on: October 28, 2022

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment.

Mukesh Soni¹, Ihtiram Raza Khan², Sameer Basir³

¹Department of CSE, University Centre for Research & Development, Chandigarh University, Mohali, Punjab 140413, India.

Computational Intelligence and Neuroscience

|July 11, 2022

Summary

This summary is machine-generated.

Industry 4.0 enables improved equipment reliability via problem detection. This study introduces an online defect detection model using long short-term memory neural networks (LSTM) for real-world industrial environments.

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Area of Science:

Industrial Engineering
Artificial Intelligence
Machine Learning

Background:

Industry 4.0 and the Internet of Things (IoT) offer opportunities to enhance industrial equipment reliability.
Complex, dynamic device interrelationships in real-world industrial settings challenge unified condition monitoring models.
Effective equipment defect detection is crucial for maintaining operational efficiency and safety.

Purpose of the Study:

To develop an online method for detecting system failures in industrial equipment.
To address the limitations of unified models in dynamic industrial environments.
To implement a robust fault detection system deployable in natural production settings.

Main Methods:

Utilized deep learning, specifically long short-term memory (LSTM) neural networks, for fault detection.
Employed curve alignment for feature extraction from sensor data.
Integrated sliding window technology for online model detection and updates.

Main Results:

Developed and validated an online defect detection model using LSTM neural networks.
Demonstrated the method's efficacy through experiments using real power plant sensor data.
Successfully identified and addressed equipment defects in a simulated production environment.

Conclusions:

The proposed LSTM-based online detection method effectively identifies equipment defects in industrial settings.
The approach provides a reliable solution for improving industrial equipment reliability within Industry 4.0 frameworks.
The integration of curve alignment and sliding window technology enhances the model's online detection and update capabilities.