Sliding window constrained fault-tolerant filtering of compressor vibration data
Data files
Feb 04, 2025 version files 381.53 KB
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fdata1case1.csv
11.94 KB
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fdata1case2.csv
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fdata1case3.csv
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fdata2case1.csv
11.59 KB
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fdata2case2.csv
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fdata2case3.csv
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fdataftffiltered.csv
33.78 KB
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fdatasgfiltered.csv
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fdatasmedfiltered.csv
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fdatasmfiltered.csv
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fwave2data.csv
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fwave2dataftffiltered.csv
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fwave2datasgfiltered.csv
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fwave2datasmedfiltered.csv
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fwave2datasmfiltered.csv
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fwavedata.csv
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fwavedataftffiltered.csv
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fwavedatasgfiltered.csv
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fwavedatasmedfiltered.csv
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fwavedatasmfiltered.csv
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normaldata.csv
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normaldatafiltered.csv
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normalwave2data.csv
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normalwave2datafiltered.csv
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normalwavedata.csv
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normalwavedatafiltered.csv
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README.md
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Abstract
This paper presents a sliding window constrained fault-tolerant filtering method for sampling data in petrochemical instrumentation. The method requires the design of an appropriate sliding window width based on the time series, as well as the expansion of both ends of the series. By utilizing a sliding window constraint function, the method produces a smoothed estimate for the current moment within the window. As the window advances, a series of smoothed estimates of the original sampled data is generated. Subsequently, the original series is subtracted from this smoothed estimate to create a new series that represents the differences between the two. This difference series is then subjected to an additional smoothing estimation process, and the resulting smoothed estimates are employed to compensate for the smoothed estimates of original sampled series. The experimental results indicate that, compared with sliding mean filtering, sliding median filtering, and Savitzky-Golay filtering, the method proposed in this paper can more effectively filter out random errors and reduce the impact of outliers when dealing with sampling data contaminated by noise and outliers. It possesses strong fault tolerance and the ability to extract the true variations of the sampling data.
README: Sliding window constrained fault-tolerant filtering of compressor vibration data
https://doi.org/10.5061/dryad.pc866t20z
Description of the data and file structure
Data type
Files containing ‘fdata1case1’ in the file represents the case "1" of the location of the outlier in the measured data "1", and so on;
Files containing ‘fwavedata’ in the file name are wave signals with outliers;
Files containing ‘fwave2data’ in the file name are polynomial signals with outliers;
Files containing ‘normaldata’ in the file name are normal measured data;
Files containing ‘normalwavedata’ in the file name are normal wave signals;
Files containing ‘normalwave2data’ in the file name are normal polynomial signals;
Files containing ‘ftffiltered’ in the file name indicate that the data have been processed by sliding-window constrained error-tolerant filtering;
Files containing ‘sgfiltered’ in the file name indicate data after Savitzky-Golay filtering;
Files containing ‘smfiltered’ in the file name represent data after sliding mean filtering;
Files containing ‘smfiltered’ in the file name indicate that the data have been processed by sliding median filtering.