Hardware-In-the-Loop Simulation of UAV for Fault Injection

Unmanned aerial vehicles (UAVs) are widely used in military and civilian applications. The safety of UAV has been paid more and more attention. Prognostic and Health Management (PHM) of UAV can realize the fault prediction during flight and make an appropriate response according to potential faults. Therefore, the safety and reliability of UAV are improved. However, the existing PHM research on UAVs has the following two challenges: a) the number of fault samples in historical flight data is small, and it is impossible to cover multiple fault modes of UAVs to meet the demand of modeling and verification, b) the actual flight verification of the UAV PHM technology cannot be carried out directly through actual flight test. Aiming at the above problems, this paper proposes a fault data generation method based on the Hardware-In-the-Loop Simulation (HILS) technology. We construct a UAV model and its corresponding fault models by analyzing fault features of UAV flight control system. It can simulate the actual faults and generate flight data with multiple fault modes, thereby available data are provided to support PHM research. Besides, PHM algorithms implemented on airborne PHM modules can be verified via this platform in real-time. The experiment results show that the HILS platform has a good performance for fault injection.

[1]  Dawei Pan,et al.  Hybrid data-driven anomaly detection method to improve UAV operating reliability , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).

[2]  Yu Peng,et al.  Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions , 2019, International Journal of Electrical Power & Energy Systems.

[3]  Xinwei Li,et al.  Design of Prognostic and Health Management Structure for UAV System , 2011, 2011 21st International Conference on Systems Engineering.

[4]  Hongzheng Fang,et al.  UAV engine fault and diagnosis with parameter models based on telemetry data , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).

[5]  Mengmeng Liu,et al.  Research on fault injection technology for embedded software based on JTAG interface , 2016, 2016 11th International Conference on Reliability, Maintainability and Safety (ICRMS).

[6]  Yu Peng,et al.  Data-Driven Remaining Useful Life Prediction Considering Sensor Anomaly Detection and Data Recovery , 2019, IEEE Access.

[7]  Yu Peng,et al.  Quantitative selection of sensor data based on improved permutation entropy for system remaining useful life prediction , 2017, Microelectron. Reliab..

[8]  Chen Yang,et al.  Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery , 2017, Microelectron. Reliab..

[9]  Yu Peng,et al.  On-line life cycle health assessment for lithium-ion battery in electric vehicles , 2018, Journal of Cleaner Production.

[10]  Chris J. Price,et al.  Automated FMEA based diagnostic symptom generation , 2012, Adv. Eng. Informatics.

[11]  Yew Chai Paw Synthesis and validation of flight control for UAV. , 2009 .

[12]  Ivo Paixao de Medeiros,et al.  Integrated task assignment and maintenance recommendation based on system architecture and PHM information for UAVs , 2015, SysCon.

[13]  Datong Liu,et al.  UAV Sensor Fault Detection Using a Classifier without Negative Samples: A Local Density Regulated Optimization Algorithm , 2019, Sensors.

[14]  Min Wang,et al.  Study on real-time fault injection and simulation of mechanic-electronic-hydraulic control system based on AMESim and LabVIEW , 2014, 2014 Prognostics and System Health Management Conference (PHM-2014 Hunan).

[15]  Chao Fu,et al.  Hardware in-the-loop simulation system based on NI-PXI for operation and control of microgrid , 2014, 2014 9th IEEE Conference on Industrial Electronics and Applications.

[16]  Young-shin Kang,et al.  Flight test of flight control performance for airplane mode of Smart UAV , 2012, 2012 12th International Conference on Control, Automation and Systems.