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Related Concept Videos

Fault Types01:18

Fault Types

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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...
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Directional relays, essential for managing unidirectional fault currents, enhance the safety and efficiency of power systems. On power lines equipped with directional relays, faults downstream (to the right) of the current transformer typically cause the fault current to lag the bus voltage by approximately 90 degrees, known as the forward direction. In contrast, upstream (left-side) faults may result in the fault current leading the bus voltage by nearly 90 degrees, termed the reverse...
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Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Bidirectional deep recurrent neural networks for process fault classification.

Gavneet Singh Chadha1, Ambarish Panambilly1, Andreas Schwung1

  • 1Department of Automation Technology, South Westphalia University of Applied Sciences, Soest, Germany.

ISA Transactions
|July 21, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces bidirectional recurrent neural networks for improved time series fault diagnosis. This approach enhances fault detection over longer periods, boosting system productivity and preventing breakdowns.

Keywords:
Bidirectional long-short term memoryCondition monitoringDeep learningFault detection and classificationRecurrent neural networksTime series analysis

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Industrial Process Control

Background:

  • Condition monitoring and fault diagnosis are critical for industrial systems.
  • Traditional methods struggle with complex fault relationships over extended time horizons.
  • Existing recurrent neural network architectures have limitations in handling sequential data for fault classification.

Purpose of the Study:

  • To present a novel time series-based approach for condition monitoring and fault diagnosis using bidirectional recurrent neural networks.
  • To improve fault detection capabilities by considering fault relations over longer time horizons.
  • To enhance network generalization and training efficiency through a new data preprocessing and restructuring procedure.

Main Methods:

  • Application of bidirectional recurrent neural networks (BRNNs) for time series fault diagnosis.
  • Development of a novel data preprocessing and restructuring technique to improve generalization and data utilization.
  • Comparative analysis of Bidirectional Long Short Term Memory (BiLSTM) networks against vanilla Recurrent Neural Networks (RNNs), Long Short Term Memories (LSTMs), and Gated Recurrent Units (GRUs).
  • Validation on the Tennessee Eastman benchmark process for both binary and multi-class fault classification.

Main Results:

  • The proposed Bidirectional Long Short Term Memory network demonstrated superior performance compared to standard recurrent architectures.
  • Experimental results showed enhanced average fault detection capability for BiLSTMs.
  • The novel data preprocessing method contributed to more efficient network training and better generalization.
  • The approach proved effective for both binary and multi-class fault classification tasks.

Conclusions:

  • Bidirectional recurrent neural networks offer a significant advancement in time series-based condition monitoring and fault diagnosis.
  • The proposed BiLSTM approach, combined with enhanced data handling, provides superior fault detection accuracy.
  • This methodology contributes to avoiding critical process breakdowns and increasing overall system productivity.