A dual-task operation and maintenance model for servo turret power head featuring dynamic combination of multi-scale shared and private features

Abstract Existing intelligent operation and maintenance (O&M) models require separately establishing regression and classification models for remaining useful life (RUL) prediction and fault diagnosis near the end of adjacent equipment degradation, making it difficult to simultaneously address both tasks and establish an effective associative model with low information utilization efficiency. We propose a dual-task O&M model for the servo turret power head, which dynamically combines multi-scale shared features and private features. This method adopts a ‘shared-private’ collaborative learning approach, processing dual tasks in parallel through a modular structure to ensure task information interaction while maintaining feature independence. The model comprises three core modules: a public multi-scale shared feature extraction module that utilizes a multi-scale convolutional kernel parallel architecture to mine multi-scale features, and outputs robust shared features after feature fusion and encoding; a dynamic private feature module that employs a dual-path parallel structure to construct independent branches for classification and regression tasks, outputting private features tailored to task requirements through ‘spatial-channel’ dual-path feature extraction and adaptive weighted fusion; and a dual-task decision-making module that includes a classifier and a regressor to independently map private features. Experimental validation demonstrates that the proposed method achieves an average root mean square error of 0.087 in RUL prediction and an average diagnostic accuracy of 98.52% in fault diagnosis for the servo turret power head. This proves that the proposed method can effectively accomplish both RUL prediction and diagnostic tasks simultaneously.