The triplex reciprocating drilling pump is a critical piece of equipment in drilling platforms, and the operational condition of its core component—the valve body—directly affects the pump’s performance and the stability of the entire system. Therefore, accurate prediction of the valve body’s Remaining Useful Life (RUL) is of great significance for ensuring the safe operation of drilling pumps and enabling predictive maintenance. However, achieving this goal involves two major challenges: (1) The complex degradation process of the valve body, which involves strong impact loads, nonlinear wear, and coupling effects between fluid and mechanical systems, makes it difficult to establish a stable degradation model and achieve accurate RUL prediction. (2) There is a lack of publicly available real-world datasets for research purposes. To address these challenges, we propose CEEMDAN-BWO-optimized Bidirectional LSTM for Remaining Useful Life prediction (CB2-RUL). The method first applies Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to the raw vibration signals for decomposition and denoising, thereby improving signal stationarity and enhancing feature representation. Next, the Black Widow Optimization (BWO) algorithm is employed to automatically tune key hyperparameters of a Bidirectional Long Short-Term Memory (BiLSTM) network. Finally, the optimized BiLSTM captures the temporal evolution patterns of valve-body degradation and produces high-accuracy RUL estimates. Finally, to verify the effectiveness of the proposed approach, we constructed a real-world dataset named VB-Lifecycle, which comprises ten valve bodies from different positions within the equipment and spans the complete lifecycle from pristine condition to failure. Extensive experiments conducted on the VB-Lifecycle dataset demonstrate that the proposed method provides accurate RUL prediction for valve bodies.
Chen et al. (Mon,) studied this question.