Using Non-stationary Time-Domain Statistical Measures for Predictive Maintenance of Large Centrifugal Compressors in the Absence of Component Faults

Citation: Kihong Shin, "Using Non-stationary Time-Domain Statistical Measures for Predictive Maintenance of Large Centrifugal Compressors in the Absence of Component Faults", Universal Library of Innovative Research and Studies, Volume 01, Issue 02.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Time-domain statistical measures such as standard deviation, skewness, and kurtosis are simple yet effective tools for assessing the health of rotating machinery based on measured vibration signals. These statistical quantities are, therefore, widely used in industry when designing condition-based maintenance (CBM) systems, especially for rotating machinery. Despite the growing adoption of CBM systems, determining the overhaul schedule can still be challenging, particularly when the rotating machinery in question does not exhibit any fault symptoms. As a result, the overhaul schedule is often determined using the traditional time-based maintenance (TBM) approach. In this short communication, we examine the time-varying characteristics of statistical quantities immediately before and after the overhaul of a large centrifugal compressor. It was found that the non-stationarity of the standard deviation increases in cases where an overhaul is due, while the variability of skewness and kurtosis does not change significantly in the absence of specific fault components.

Keywords: Non-Stationary, Vibration Signal, Condition Monitoring, Centrifugal Compressor, Rotating Machinery.

https://doi.org/10.70315/uloap.ulirs.2024.0102003