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。有很多不同的技巧。但我很困惑，因为我不知道从哪里开始。你知道吗

根据我的研究，首先重要的是，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。（但是我怎样才能搜索来学习所有的技巧呢？我并没有在同一页中看到所有的技术。）

然后我开始统计计算来处理。你知道吗

我没有画路线图。你能帮助这些问题或建议书或什么？你知道吗

Answer 1

欢迎来到SO。。。要利用此站点，请将鼠标悬停在问题的标签fft上方。。。然后单击View tag。。。然后按learn more。。。在阅读完fft上的信息页面后，点击Votes查看这里投票率最高的帖子。。。这些问题/答案将使你进入球场

我强烈建议你掌握这里解释的细节Discrete Fourier Transform - Simple Step by Step

傅里叶变换交互式指南
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

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

How to get frequency from fft result?

我可以继续从我的笔记中提到掘金，但我会留下这本优秀书籍的节选

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