离心故障检测与卷积神经网络转移学习
Cem Ekin Sunal1, Vladan Velisavljevic1, Vladimir Dyo2
1School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究引入了一种新的方法,用于检测离心故障,使用现实世界的运行数据和残余网络. 这种方法在从DQ/Concordia模式分类故障方面取得了85.51%的准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 离心在工业和家庭应用中至关重要,但故障可能会造成重大财务损失和安全风险.
- 早期故障诊断和预测性监测对于可靠的运行至关重要.
- 现有的机器学习故障检测方法通常依赖于合成或实验室数据,限制了现实世界的适用性.
研究的目的:
- 开发和评估基于机器学习的系统,用于检测真实运行的离心中的故障.
- 解决在以前的故障检测研究中使用合成或模拟数据的局限性.
- 将图像检测的转移学习应用于实际的工程问题.
主要方法:
- 利用来自不同地点实际运行的离心收集的数据.
- 从DQ/Concordia模式中使用视觉特征的二进制分类.
- 实现了一个剩余网络 (ResNet-34) 从图像检测域进行转移学习.
主要成果:
- 在检测离心故障方面达到高达85.51%的分类准确度.
- 证明了使用真实操作数据用于故障诊断的有效性.
- 成功地应用转移学习来解决现实世界的工程故障检测问题.
结论:
- 拟议的方法有效地检测离心故障使用真实运行数据和残余网络.
- 转移学习可以提高工程应用中的故障检测系统的性能.
- 这项研究为工业环境中离心的预测性维护提供了一种可行的方法.
相关概念视频
State Space to Transfer Function
198
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
198
Transfer Function to State Space
247
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
In an...
247
Convolution Properties II
192
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
192
Transfer Function in Control Systems
469
The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
To derive the transfer function, consider a general nth-order linear time-invariant...
469


