Valve Failure Prognostics In Reciprocating Compressors Utilizing Temperature Measurements, PCA-based Data Fusion And Probabilistic Algorithms

In the present paper, temperature measurements are utilized to develop health indicators based on principal component analysis towards the probabilistic estimation of the Remaining Useful Life (RUL) of reciprocating compressors in service. Temperature degradation histories obtained from thirteen actual valve failure cases constitute the training data in a data-driven prognostic approach. Two data-driven prognostic methodologies are presented and proposed based on probabilistic mathematical models i.e. Gradient Boosted Trees (GBTs) and Non-Homogeneous Hidden Semi Markov Models (NHHSMM). The training and testing process of all models is described in detail. RUL prognostics in unseen data are obtained for all models. Beyond the mean estimates of the RUL, the uncertainty associated with the point prediction is quantified and upper/lower confidence bounds are also estimated. Prediction estimates for twelve real-life failure cases are presented and the pros and cons of each model’s performance are highlighted. Several metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding the prognostic capabilities of each of them.