Predicting machine activity of forwarders using J1939 CAN bus data and machine learning

As the New Zealand forestry industry progresses toward the implementation of Forestry 4.0 and 5.0 concepts, the use of machine data for operational monitoring and analysis is becoming increasingly valuable. This study investigates the application of machine learning to automate time studies of forwarders by predicting machine activity using Controller Area Network data. Data were collected from two John Deere 1910E forwarders over three harvesting sites (295 cycles) in the South Island of New Zealand. Four machine learning models, Random Forest, Classification and Regression Tree, Long Short-Term Memory, and Gated Recurrent Unit, were trained on data of Forwarder One at an 80:20 (training:testing) split (185 cycles). The models were then further evaluated for generalisability using data of Forwarder Two (110 cycles). Initial trials revealed that machine learning models struggled to distinguish between machine activities such as Loading and Unloading or Driving Loaded and Unloaded, with the neural network models (Long Short-Term Memory and Gated Recurrent Unit) demonstrating superior performance in capturing temporal dependencies. After relabelling and reducing feature complexity, the Gated Recurrent Unit model achieved a validation accuracy of 95.51%, outperforming all other models. Comparison with existing methods from the literature showed that this model provided lower error rates for cycle time, with a percent error of 0.05% and a Root Mean Square Error of 0.02 minutes. At the machine activity level, the model was significantly better, with a decrease in percent error of up to approximately 130% and Root Mean Square Error of up to approximately 1.8 minutes, compared to previous literature. This research demonstrates the feasibility of using machine learning to automate forwarder time studies with high accuracy, providing a scalable and replicable framework that could extend to other forestry machinery. The findings support the integration of these models into Cyber-Physical Systems for real-time monitoring, though challenges such as data access and model generalisation must be addressed to fully realise the benefits of Forestry 4.0.