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Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

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Related Experiment Video

Updated: Jul 24, 2026

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

Seq2Seq-based GRU autoencoder for anomaly detection and failure identification in coal mining hydraulic support

Kai Zhan^1,2, Cong Wang^3,4, Xigui Zheng^5,6,7,8

¹Shandong Succeed Mining Safety Engineering Co. Ltd, Jinan, China.

Scientific Reports

|January 3, 2025

Summary

This study introduces a Gated Recurrent Unit Autoencoder (GRU-AE) for detecting anomalies and diagnosing faults in coal mine hydraulic support pressure data. The model effectively identifies potential equipment failures, enhancing mine safety.

Keywords:

Anomaly detection Autoencoder Gated recurrent unit Hydraulic support Mining engineering

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Area of Science:

Mining Engineering
Data Science
Machine Learning

Background:

Stable operation of hydraulic supports is critical for coal mine safety.
Nonlinear, non-stationary, and noisy hydraulic support pressure data complicate anomaly detection and fault diagnosis.

Purpose of the Study:

To develop an effective anomaly detection and failure identification method for hydraulic support pressure data.
To provide an early warning system for equipment failures in coal mines.

Main Methods:

Utilized a Gated Recurrent Unit Autoencoder (GRU-AE) for anomaly detection and failure identification.
Analyzed data from two Chinese coal mines to evaluate model parameters, particularly window size.
Investigated the impact of teacher forcing techniques on model performance.

Main Results:

A window size of 144 was found to provide optimal performance for anomaly detection and failure mode identification.
The GRU-AE model demonstrated superior learning capability for periodic data patterns and equipment failure characteristics.
Teacher forcing can accelerate convergence but may reduce generalization capability.

Conclusions:

The proposed GRU-AE model offers an innovative technical solution for coal mine safety monitoring.
The model effectively identifies anomalous regions and potential equipment failure characteristics in complex hydraulic support data.
The study validates the GRU-AE model's effectiveness for hydraulic support pressure anomaly detection and equipment fault diagnosis.