Prediction of Remaining Useful Life of Packing Sets in a Plunger-Type High-Pressure Compressor Based on the PCA/SVD Analysis and NN Model

Abstract

A machine learning-based prognostic strategy is developed in this paper for predicting the remaining useful life (RUL) of high-pressure packing in plunger-type hypercompressors. The proposed strategy applies principal component analysis (PCA) to identify the three most important sensors out of 33 possible options which seem relevant to the subject of high-pressure packing. Singular-value decomposition (SVD) is then performed with respect to chronological Hankel matrices reconstructed from one of these three pieces of sensor data, namely, leakage flow. The normalised correlation coefficient between SVD eigenvalue vectors of chronological data is defined with a view to formulate health state assessment measurement. In order to enhance the prediction accuracy of the RUL of high-pressure packing, a linear regression algorithm and a two-term power series regression algorithm are both integrated into the NN (neural network) model. The effectiveness of the method is examined using the averaged difference (over 13 data sets) between predicted and real failure events. The results showed that the maximum prediction RUL error of the model is less than 15 days, and an averaged prediction RUL error is 7.23 days for 13 run-to-failure events. Additionally, a more recent test was performed using online data to examine the health states of four identical types of packing.