How to detect change in mean value of a signal

23 次查看（过去 30 天）
Danupon Subanapong
Danupon Subanapong 2019-7-28
评论： Steven Lord 2019-7-31
Hello!!!
I would like to ask for some help.
I am working with inputing a large number of time-series signals. Among these signals, there are some signals which the mean value has changed. I would ignore reading the signals which there is a change in mean value. I will give three examples of signal: 1) normal (without changing mean), 2) abrupt changing in mean value, and 3) linear changing in mean value. My goal is to detect signals which there is a change in mean and skip reading these signals.
Ps., I would like to set criteria that if the magnitude of change in mean is larger than 10,000, it will treat as there is a change of mean value. Lastly, the files of these three examples of signal are also attached.
1) Normal (✅ would like to input to matlab)
2) Abupt change (❌ do not want to input to matlab)
3) Linear change (❌ do not want to input to matlab)

请先登录，再回答此问题。

采纳的回答

Akira Agata
Akira Agata 2019-7-31
It may need to apply "smoothing" before detecting changes larger than 10,000. Looking at your data, ~2000 points movmean will clarify whether data contains changing point or not, as shown in the followign plot.
But, please note that ~2000 points moving mean will clean-up changes in short time (such as spilke noise). So, if some of your data contains such a short-time changes, you should consider some additional detection methods.
movmean.png

更多回答（2 个）

Jan
Jan 2019-7-28
编辑：Jan 2019-7-28
It is impossible to detect a change, if it occurs in the first or last few frames.
Ist there only 1 change of the mean? Then fit a line to the data and set a limit for the slope. If there can be more changes, fit a parabola or a polynomial of higher order to the data and check the factors.
Alternatively use findchangepts.
Is the sine wave with a frequency of about 4/100 the search data. Then you can filter it out at first to reduce the noise for the detection of a change in the mean.
  1 个评论
Danupon Subanapong
Danupon Subanapong 2019-7-29
Thank you so much for your suggestions. In some cases, there are more than 1 change of the mean. But the idea of fit a line is good. I will try this idea. If it works, I will inform you again.

请先登录，再进行评论。

dpb
dpb 2019-7-28
编辑：dpb 2019-7-28
The linear or similar trend is probably detectable as Jan says by fitting linear trend line and testing for nonzero slope..._IF_ the trend is something like your example and doesn't also include returning to the same or near same baseline...in that case you could have essentially zero overall slope with two trend lines buried inside.
I'd guess more robust would be to use windowing to compute means over the duration of the signal and compare those for stationarity--perhaps some preliminary smoothing might help, then again might not.
Which is the kind of thing as Jan recommends findchangepts does...I had not been aware it had been introduced to Signal Processing TB -- it's a fairly recent addition.
  2 个评论
Danupon Subanapong
Danupon Subanapong 2019-7-29
"I'd guess more robust would be to use windowing to compute means over the duration of the signal and compare those for stationarity--perhaps some preliminary smoothing might help, then again might not."
I also think about this idea. I think this also might work. I will try both of your ideas. Thank you so much for your help.
Steven Lord
Steven Lord 2019-7-31
FYI, a couple releases after findchangepts was introduced in Signal Processing Toolbox we also added ischange to MATLAB itself.

请先登录，再进行评论。

类别

在 Help Center 和 File Exchange 中查找有关 Correlation and Convolution 的更多信息

标签

产品


版本

R2018b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by