LSTM-BASED WORKLOAD RECOGNITION FOR HYDRAULIC ACTUATORS: A CASE STUDY ON EXCAVATOR DIGGING PROCESS

Hydraulic mobile machines in the construction industry, such as excavators, rely on hydraulic actuators to handle various workloads. A reliable and accurate classification of these workloads is essential for improving the safety and automation level of machine operations. Processing of hydraulic actuator operating data via a deep-learning-based classifier has great potential for time-varying workload recognition. In particular, Long Short-Term Memory (LSTM) networks demonstrate strengths in analyzing time-sequential information. In this study, a customized LSTM-based classifier is developed to perceive the uncertain and varying external workload in real-time accurately. Considering the working features of the bucket cylinder, signals including position, velocity, and pressure from both chambers of the hydraulic cylinder actuator are selected as input data for the classifier. An experimental dataset from a hydraulic actuator test bench, reflecting typical workload types of excavator digging processes, is collected. The proposed classifier achieves satisfactory accuracy in recognizing workload types in both training and test cases, demonstrating its practical advantages for excavator digging processes.