Sliding window constrained fault-tolerant filtering of compressor vibration data
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 sli...
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The Royal Society
2025-08-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.241957 |
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| author | Shaolin Hu Xianxi Chen Guoxi Sun |
| author_facet | Shaolin Hu Xianxi Chen Guoxi Sun |
| author_sort | Shaolin Hu |
| collection | DOAJ |
| description | 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 the 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. |
| format | Article |
| id | doaj-art-2a0eac0e35f943a6a87f83b227bf6aca |
| institution | Kabale University |
| issn | 2054-5703 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-2a0eac0e35f943a6a87f83b227bf6aca2025-08-20T04:01:25ZengThe Royal SocietyRoyal Society Open Science2054-57032025-08-0112810.1098/rsos.241957Sliding window constrained fault-tolerant filtering of compressor vibration dataShaolin Hu0Xianxi Chen1Guoxi Sun2Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, People’s Republic of ChinaGuangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, People’s Republic of ChinaGuangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, People’s Republic of ChinaThis 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 the 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.https://royalsocietypublishing.org/doi/10.1098/rsos.241957fault-tolerant filteringrandom erroroutliershigh fidelitynon-smooth sequences |
| spellingShingle | Shaolin Hu Xianxi Chen Guoxi Sun Sliding window constrained fault-tolerant filtering of compressor vibration data Royal Society Open Science fault-tolerant filtering random error outliers high fidelity non-smooth sequences |
| title | Sliding window constrained fault-tolerant filtering of compressor vibration data |
| title_full | Sliding window constrained fault-tolerant filtering of compressor vibration data |
| title_fullStr | Sliding window constrained fault-tolerant filtering of compressor vibration data |
| title_full_unstemmed | Sliding window constrained fault-tolerant filtering of compressor vibration data |
| title_short | Sliding window constrained fault-tolerant filtering of compressor vibration data |
| title_sort | sliding window constrained fault tolerant filtering of compressor vibration data |
| topic | fault-tolerant filtering random error outliers high fidelity non-smooth sequences |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.241957 |
| work_keys_str_mv | AT shaolinhu slidingwindowconstrainedfaulttolerantfilteringofcompressorvibrationdata AT xianxichen slidingwindowconstrainedfaulttolerantfilteringofcompressorvibrationdata AT guoxisun slidingwindowconstrainedfaulttolerantfilteringofcompressorvibrationdata |