Oil-powered plant protection unmanned helicopters play an indispensable role in modern precision agriculture due to their large payload and long endurance. However, their complex operational mechanisms, including broadband strong vibration, high-temperature engine radiation, and coupling among multiple subsystems, impose extremely high demands on condition monitoring. Existing monitoring methods mostly rely on closed flight control systems, which commonly suffer from issues such as missing on-board equipment status data, low telemetry data update rates, high transmission latency, and non-open interfaces, leading to severe deficiencies in real-time flight status monitoring and fault diagnosis capabilities. To address these problems, a multi-signal acquisition and condition monitoring system is designed that is completely independent of the original flight control system. At the hardware circuit level, by integrating an MPU6050 attitude sensor, laser rangefinder, thermocouples, and optoelectronic isolation detection circuits, the system achieves high-frequency acquisition of nine types of key parameters, including flight attitude angles, engine speed, exhaust gas temperature, generator three-phase voltage, and servo command/feedback signals (attitude at 5 Hz, status at 1 Hz). The extended MavLink protocol is used for data encapsulation, and data are transmitted to a ground station via 4G/5G networks in a time-sharing manner, combined with on-board cyclic data storage, thereby establishing a dual-redundant data link. On this basis, an engine overheating early-warning method based on a temperature-speed coupling model and a fault detection algorithm for servo actuators based on an Extended Kalman Filter (EKF) are proposed. Test results show that the system operates independently and stably, reducing the uplink latency of critical attitude data to less than 200 ms, and successfully achieving rapid identification of faults such as generator phase loss (response <1 s) and servo jamming. This system can serve as an independent on-board “black box”, providing a reliable data foundation and diagnostic tool for real-time condition monitoring, predictive maintenance, and post-flight incident analysis of oil-powered unmanned helicopters.