python - 从传感器数据到预测性维护的路线图

Question

我对这些话题很陌生。我研究了很多关于这个问题的文章。有很多不同的技术。但我很困惑，因为我不知道，从哪里开始。

根据我的研究，第一件事很重要；我必须对原始传感器数据进行预处理。有一些技术，fft就是其中之一。（但我如何搜索以学习所有技术？我没有在同一页面中看到所有技术。）

然后我开始统计计算处理。

我没有画路线图。你能帮助这些问题或建议书籍或任何东西吗？

Answer 1

第一步将是数据清理和特征提取。您需要以适用于机器学习算法的格式准备数据。我向您推荐我的论文“多功能打印机信号的通用数据插补和特征提取”。它是关于从物联网信号中准备数据以进一步应用机器学习算法。

Answer 2

欢迎来到 SO ... 利用此网站将鼠标悬停fft在您问题的标签顶部 ... 然后单击 View tag... 然后点击learn more... 然后在阅读 fft hit 上的信息页面后，在Votes此处查看投票率最高的帖子所以......这些问题/答案会让你进入球场

我强烈建议你掌握这里解释的细节离散傅里叶变换 - 一步一步简单

傅里叶变换的交互式指南
https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/

对傅里叶变换和 FFT 的直观理解
https://www.youtube.com/watch?v=FjmwwDHT98c

直观的离散傅立叶变换教程
http://practicalcryptography.com/miscellaneous/machine-learning/intuitive-guide-discrete-fourier-transform/

如何从 fft 结果中获取频率？

我可以继续从我的笔记中提到掘金，但我会给你留下一本好书的摘录

http://www.dspguide.com/ch10/6.htm

The Discrete Time Fourier Transform (DTFT) is the member of the Fourier transform family that operates on aperiodic,
discrete signals. The best way to understand the DTFT is how it relates to the DFT. To start, imagine that you
acquire an N sample signal, and want to find its frequency spectrum. By using the DFT, the signal can be
decomposed into sine and cosine waves, with frequencies equally spaced between zero and one-half of the
sampling rate. As discussed in the last chapter, padding the time domain signal with zeros makes the period
of the time domain longer, as well as making the spacing between samples in the frequency domain narrower.
As N approaches infinity, the time domain becomes aperiodic, and the frequency domain becomes a continuous signal.
This is the DTFT, the Fourier transform that relates an aperiodic, discrete signal, with a periodic,
continuous frequency spectrum