Sliding window constrained fault-tolerant filtering of compressor vibration data

TL;DR

This paper introduces a new filtering method that improves the accuracy of vibration data from compressors in petrochemical systems by reducing noise and outliers.

Contribution

A novel sliding window constrained fault-tolerant filtering method is proposed for more accurate data smoothing in noisy environments.

Findings

The proposed method outperforms sliding mean, median, and Savitzky–Golay filters in reducing random errors and outliers.

The method effectively extracts true variations in sampling data with strong fault tolerance.

Experimental results confirm the method's effectiveness in petrochemical instrumentation applications.

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 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.

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