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相关概念视频

Fault Types01:18

Fault Types

400
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...
400
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

526
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
526
Bus Impedance Matrix01:24

Bus Impedance Matrix

503
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.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
503
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

671
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...
671
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

497
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
497
Multimachine Stability01:25

Multimachine Stability

548
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
548

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相关实验视频

Updated: Jan 17, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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Published on: October 28, 2022

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数字模拟数据辅助域适应性概括方法用于故障诊断.

Tao Yan1, Jianchun Guo1, Yuan Zhou1

  • 1College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种用于机械故障诊断的新型域泛化方法,利用数值模拟数据来提高模型在不同操作条件下的适应性. 该方法提高了未见数据的诊断准确性,优于现有方法.

关键词:
域名 适应 适应 域名 适应域名通用化域名通用化错误诊断 错误诊断 错误诊断 是一个问题.有限元素模型的模型是有限的.

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相关实验视频

Last Updated: Jan 17, 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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Surrogate Model Development for Digital Experiments in Welding
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科学领域:

  • 机械工程 机械工程
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 机械故障诊断在不同的操作条件下面临跨领域分布转移的挑战.
  • 现有的域自适应方法需要目标数据,限制实时应用.
  • 将故障特征从源到未见的目标域进行概括对于机械故障检测至关重要.

研究的目的:

  • 开发用于机械故障诊断的域泛化方法,克服对目标数据的需求.
  • 提高故障诊断模型对分布外数据的概括能力.
  • 提高不同操作环境中的故障诊断系统的准确性和稳定性.

主要方法:

  • 一个有限元模型 (FEM) 作为辅助领域生成了数值模拟数据.
  • 集成辅助域数据与现实世界的测量数据,以创建一个多源域.
  • 在多源域上使用对抗性培训,以学习域不变特征.

主要成果:

  • 与基线方法相比,拟议的方法显示出优越的概括性能.
  • 在轴承故障诊断方面实现了2.83%的平均准确性改进.
  • 在轮故障诊断方面实现了8.9%的平均准确性改进.

结论:

  • 开发的域泛化技术有效地解决了机械故障诊断中的跨域分布偏移.
  • 将模拟和现实数据与对抗训练相结合,可增强对未见条件的模型概括性.
  • 该方法为实时,强大的机械故障检测提供了可行的策略.