Integrated operational monitoring and fault early warning system for wheat combine harvesters based on CAN bus

The core objective of this study is to address critical challenges in the operational monitoring and fault early warning of wheat combine harvesters. To this end, this study designed a field-oriented multi-parameter detection system for wheat combine harvesters, which utilizes the CAN bus and virtual instrumentation. Key challenges in this field include three aspects: first, manual inspection is inefficient and lacks automated detection methods, making it difficult to meet the real-time requirements of large-scale operations; second, fault early warning accuracy is low, as single-parameter evaluation is prone to false positives and false negatives; third, monitoring parameters function in isolation, leading to significant data inconsistencies that hinder the early detection of potential faults. To address these issues, this study focuses on three key tasks: establishing a multi-parameter collaborative monitoring framework, optimizing hardware and communication protocols, and developing data processing methods for fault detection and warning. Specifically, sensors for fuel consumption, Hall-effect rotational speed, and strain-gauge torque are deployed at critical components of the harvester. The system then efficiently transmits operational status data via the CAN bus to a processing module, enabling remote real-time monitoring of the harvester’s comprehensive operational conditions. For the designed fault warning algorithm, it dynamically adjusts warning thresholds by comparing characteristic parameters with historical data, thereby achieving accurate fault identification and timely warning responses. This study innovatively transmitted multi-source sensor data through the high-anti-interference CAN bus and developed a fault warning algorithm incorporating feature recognition and dynamic thresholds. In simulated experiments, the measurement errors of both instantaneous and cumulative fuel consumption were ≤5%, while the system achieved a warning accuracy of 97.3% and a response time of ≤180 ms. This represents a 15.3-percentage-point improvement in accuracy compared to traditional single-parameter warning systems. Overall, this study addresses the challenge of multi-parameter integrated monitoring for wheat combine harvesters and provides a scalable technical solution for hardware integration and comprehensive data analysis. It also offers a reference for the intelligent upgrading of Chinese harvesters, which is expected to accelerate the transformation of agricultural mechanization toward precision and informatization.