An improved LSTM-based model for identifying high working intensity load segments of the tractor load spectrum

Abstract

In order to solve the problems of serious redundancy of existing load spectrum data, unclear identification of load segments corresponding to high working intensity, and failure of load spectrum interception to meet the requirements of heavy load conditions, this paper proposes an MHA-ConvLSTM (Multi-head attention Convolutional LSTM) network model for identifying high working intensity load segments of the tractor load spectrum. The deep learning model integrates a multi-head attention mechanism and ConvLSTM network as the core, keeping the temporal order of continuously changing load sequences as the basic principle, deeply mining the local features contained within the small range load of dynamically changing load, and strengthening the matching relationship of the intrinsic features in long distance and large data volume load. This research selects rotary tillage as the verification condition, builds the multi-sensor test system to carry out the working load test, and takes the tractor rotary tillage load spectrum data as the validation object. The analysis shows that the accuracy and F1-score of the MHA-ConvLSTM model reach 97.69% and 97.83%, respectively, and the operation time is only 0.5289 s, which is 15.82% faster than LSTM. In addition, the model in this paper was used to identify 399 load segments with high workload and high work intensity, and 388 load segments were successfully verified with an error rate of less than 3%. This paper provides a new technical solution for applying the agricultural equipment load spectrum.