This work presents a systematic process for building a Fault Diagnoser (FD), based on Petri Nets (PNs) which has been applied to a small helicopter. This novel tool is able to detect both intermittent and permanent faults. The work carried out is discussed from theoretical and practical point of view. The procedure begins with a division of the whole system into subsystems, which are the devices that have to be modeled by using PN, considering both the normal and fault operations. Subsequently, the models are integrated into a global Petri Net diagnoser (PND) that is able to monitor a whole helicopter and show critical variables to the operator in order to determine the UAV health, preventing accidents in this manner. A Data Acquisition System (DAQ) has been designed for collecting data during the flights and feeding PN diagnoser with them. Several real flights (nominal or under failure) have been carried out to perform the diagnoser setup and verify its performance. A summary of the validation results obtained during real flight tests is also included. An extensive use of this tool will improve preventive maintenance protocols for UAVs (especially helicopters) and allow establishing recommendations in regulations.
This work presents a Fault Diagnosis application based on Petri Nets (PN) applied to a small unmanned helicopter. The first step of the research is the construction of the model and diagnoser for the Helicopter by using PN. A Data Acquisition System (DAQ) has been designed and built for providing the PN Diagnoser with data during the flights. Missions have been conducted with the aircraft configured to fly in both normal and fault operation. Thus, several common faults were intentionally generated during the test flights. This application allows the operator to perform the aerial vehicle health monitoring in order to prevent major damages in case of accident. Vehicle variables are monitored and thresholds adequate for the UAV defined. A summary of the validation results obtained during real flight tests are also included. An extensive use of this tool would allow improving preventive maintenance protocols for UAVs and establishing recommendations in regulations. UAVs accidents involve not only high economic cost but also serious restrictions for performing flights over populated areas. This work integrates Fault Diagnosis from theoretical and practical point of view. The use of the diagnoser by using Petri Nets is considered as novel approach.
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