滚动轴承的寿命预测基于最佳时间频谱和DenseNet-ALSTM
Jintao Chen1, Baokang Yan1, Mengya Dong1
1School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
这项研究引入了一种先进的方法,通过增强振动信号和优化时间频谱来预测滚动轴承寿命. 该方法显著提高了机械健康监测的预测准确性.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 滚动轴承的寿命预测受到杂的时间信号的挑战,阻碍了故障特征提取并降低了准确性.
- 有效的故障诊断和剩余的使用寿命估计对于工业机械维护至关重要.
研究的目的:
- 开发一种可靠的方法来预测滚动轴承的寿命,克服噪声干扰.
- 为了增强故障特征提取和提高预测准确度,使用最佳的时间频率表示.
主要方法:
- 使用CEEMDAN (互补组合实证模式分解) 和Teager能量操作员来消除和增强振动信号的信号重建.
- 使用蛇优化器 (SO) 优化通用S变换 (GST) 时间频谱,以获得最佳的时间频谱.
- 使用DenseNet-ALSTM网络进行寿命预测,采用最佳时间频谱集.
主要成果:
- 拟议的方法在滚动轴承寿命评估中表现出高的预测准确性.
- 对比和废弃实验验证了开发方法的有效性和理想性能.
- 信号增强和最佳时间频谱分析有助于改善预测结果.
结论:
- 集成CEEMDAN,Teager能源运营商,SO优化的GST和DenseNet-ALSTM,为滚动轴承的寿命预测提供了一个卓越的解决方案.
- 这种方法有效地解决了振动信号中的噪声挑战,从而实现了更可靠的机械健康监测.
- 该研究强调了先进的信号处理和深度学习的潜力,以实现准确的预测性维护.
相关概念视频
01:08Determination of Expected Frequency
2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
01:15Discrete Fourier Transform
275
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
275
01:20Discrete-Time Fourier Series
262
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
For a discrete-time periodic signal x[n]...
262
01:18Aliasing
133
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
133
01:15Construction of Frequency Distribution
7.7K
A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
7.7K
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K