研究基于声音信号和GA-SVM-RFE的螺栓松动识别
1School of Civil Engineering and Architecture, Jishou University, Zhangjiajie, 427000, China.
Scientific reports
|July 7, 2025
概括
本研究引入了一种新的方法,用于检测钢结构中的螺栓松动,使用声音分析和先进的机器学习. 该方法实现了高精度,为结构安全监控提供了实用解决方案.
科学领域:
- 结构工程 结构工程
- 声学 声学 在声学方面
- 机器学习 机器学习
背景情况:
- 在钢筋结构中检测螺栓连接损坏存在重大挑战.
- 现有的方法在识别微妙损坏 (如螺栓松动) 时可能缺乏准确性或效率.
研究的目的:
- 开发和验证一种可靠的方法来识别钢筋结构中螺栓松动的情况.
- 利用声信号分析与机器学习相结合,以加强结构健康监测.
主要方法:
- 来自钢筋结构的声音信号进行了预处理和分析.
- 提取了关键特征,包括短期能量,零交叉率和波形包能量.
- 支持矢量机递归特征消除 (SVM-RFE) 和基因算法优化支持矢量机 (GA-SVM) 用于特征选择和模型优化.
主要成果:
- 拟议的GA-SVM方法实现了99.5%的高识别精度.
- 使用SVM-RFE.有效的特征提取和选择被证明是有效的.
- 该方法在钢筋结构的螺栓松动测试中取得了成功.
结论:
- 声信号分析和GA-SVM方法为螺栓松动检测提供了一个高度准确和可行的解决方案.
- 这种方法在工程结构的安全监测中具有实践应用的巨大潜力.
- 该研究为确保钢结构结构系统的完整性提供了宝贵的技术支持.
相关概念视频
Classification of Signals
908
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
908
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations
1.2K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.2K
Signal Sequences and Sorting Receptors
6.0K
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
6.0K
Instrument Calibration
273
Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
273
Force Classification
1.6K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.6K
Detection of Gross Error: The Q Test
6.4K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.4K


