检测车片缺陷的综合方法使用组图和波形特征与嵌套的二分法家族分类器
Sakthivel Gnanasekaran1,2, Lakshmi Pathi Jakkamputi2, Jegadeeshwaran Rakkiyannan2
1School of Mechanical Engineering, Vellore Institute of Technology, Chennai 600127, India.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
持续监测液压制动系统对于商用车辆的安全至关重要. 这项研究表明,使用振动信号和先进的算法来检测车缺陷的方法准确率为99.45%.
科学领域:
- 机械工程 机械工程
- 汽车工程 汽车工程
- 信号处理 信号处理
背景情况:
- 液压制动系统是商用车辆的关键安全部件.
- 持续监测对于确保可靠的制动系统性能至关重要.
- 振动分析为状态监测提供了一个有前途的非侵入性方法.
研究的目的:
- 开发和评估一种使用振动信号监控液压制动系统的方法.
- 根据提取的特征,准确地分类制动条件 (好与坏).
- 为此应用确定最有效的分类算法.
主要方法:
- 在正常和缺陷条件下,从商用车辆车片组件中捕获振动信号的实验捕获.
- 提取相关特征,包括直方图和波形特征.
- 使用嵌套二分法家族算法的提取特征的分类.
主要成果:
- 波形特征与类平衡的嵌套二分法算法相结合,实现了最高的准确性.
- 获得了99.45%的最大分类准确率.
- 拟议的方法有效地区分健康和缺陷的制动系统.
结论:
- 振动信号分析,特别是使用波形特征和高级分类,为液压制动系统监控提供了高度准确的方法.
- 这种方法可确保商用车辆制动系统的可靠性和安全性.
- 实施这种监控技术可以提高车辆的整体安全性和运行效率.
更多相关视频
相关概念视频
Relative Frequency Histogram
5.5K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
5.5K
Histogram
13.3K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
13.3K
Classification of Signals
472
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...
472
Probability Histograms
11.7K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.7K
Classification of Systems-II
149
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
149
Quantifying and Rejecting Outliers: The Grubbs Test
1.6K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K


