Work‐phase recognition in construction machinery using gated recurrent unit with attention and fractional calculus features

Abstract

Accurate work‐phase recognition is essential for advancing energy efficiency and intelligent control. However, significant challenges impede the advancement of work‐phase recognition technology, including the complexity of sensor input signals, reliance on manual intervention for time‐frequency feature selection, limited model generalization, and suboptimal recognition accuracy. To address these issues, this paper proposes a deep learning framework that combines a feature fusion method that integrates gated recurrent unit (GRU) network feature extraction and fractional calculus feature (FCF) enhancement with a Bayesian‐optimized random forest (RF) classifier. A GRU network with an integrated attention mechanism effectively reduces the need for manual feature selection, whereas FCF enhancement expands the feature space through fractional integration and differentiation without additional sensors. Feature‐level data fusion and Bayesian optimization improve the generalization capability of the RF model. The experimental results for two typical types of machinery demonstrated recognition accuracies of 99.38% and 99.45% for work‐phase recognition, confirming the superior performance of the proposed framework.