The hybrid velocity prediction model for pipeline detection based on bidirectional long short-term memory and an improved attention mechanism

Speed prediction of pipeline detectors is crucial for accurate pipeline positioning and defect detection. This paper proposes a novel hybrid prediction model for this purpose, combining dual-layer Bidirectional Long Short-Term Memory, a bidirectional input attention mechanism, Singular Spectrum Analysis, and Bayesian Optimization. The dual-layer Bidirectional Long Short-Term Memory captures both forward and backward information in time series for prediction. The attention mechanism assigns weights to multiple input features. Singular Spectrum Analysis reconstructs and extracts features from time series data, while Bayesian Optimization is used to obtain the optimal hyperparameters for the Singular Spectrum Analysis and Bidirectional Long Short-Term Memory algorithms. A pipeline experimental platform was constructed to conduct comparative tests of the proposed model under constant speed, variable speed, non-lubricated, and lubricated conditions, assessing both operation and prediction. The results indicate that, compared to the baseline model, the hybrid prediction model proposed in this paper achieves improvements of over 9 % in Root Mean Square Error and over 0.7 % in R-Square under the most severe variable-speed conditions, demonstrating superior performance in prediction accuracy and generalization capability.