python中的振动数据FFT
我是机器健康/物联网分析的新手，但是我正在尝试比较机器四个不同位置的加速度计传感器产生的频率。每个传感器都会产生X和Y输出CSV，因此总共有8个CSV。以半公路间隔（〜2小时）收集测量值，但距离越来越小。
每个CSV都是相同的，其中每一行具有时间戳，采样率，样品长度，然后在后续列中数据。 The sample rate is 8192Hz with sample length of 4096.
My thought is to use FFT to identify the dominant frequencies for each timestamp and then compare the frequencies over time to evaluate the performance.
我不确定是否可以FFT，因为时间间隔不是100％一致的。我也不知道用于变量的数字。任何帮助都将受到赞赏。
我尝试为每个时间戳平均数据平均，然后随着时间的推移比较平均值，但这并没有透露任何有见地的信息。我希望看到高活性和无活动的静态点的峰值。取而代之的是，我的平均值基本上是波浪。
我希望看到揭示高振动的峰值，因此活性和低点（无/最小活动）。
I'm new to machine health/IoT analytics, but I'm trying to compare the frequencies produced by accelerometer sensors at four different locations of a machine. Each sensor produces an X and a Y output CSV so in total there are 8 CSVs. Measurements are collected at semi-routine intervals (~2 hours) but there are some bigger and smaller gaps.
Each CSV is the same where each row has a timestamp, sampling rate, sample length, and then data in the subsequent columns. The sample rate is 8192Hz with sample length of 4096.
My thought is to use FFT to identify the dominant frequencies for each timestamp and then compare the frequencies over time to evaluate the performance.
I'm not sure if I can FFT because the time intervals aren't 100% consistent. I also don't know what numbers to use for the variables. Any help is appreciated.
I've tried averaging the data for each timestamp and then comparing the averages over time, but this didn't reveal any insightful information. I was hoping to see peaks of high activity and static points of no activity. Instead, my averages were basically a wave.
I'm hoping to see peaks which reveal high vibration, thus activity, and low points (no/minimal activity).
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