Fitness function determination of uav anomaly detection in large data set via pso
This project is based on fitness function determination of Unmanned Aerial Vehicle (UAV) anomaly detection in large data set. Fitness function is a solution to the issue as input and outputs how "fit" or "excellent" the answer is with regard to the problem under discussion. Based...
| Main Author: | |
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39010/ http://umpir.ump.edu.my/id/eprint/39010/1/EA18061_FATIMAH%20DAING%20JAMIL_THESIS%20-%20Fatimah%20Daing.pdf |
| _version_ | 1848825658471350272 |
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| author | Fatimah, Daing Jamil |
| author_facet | Fatimah, Daing Jamil |
| author_sort | Fatimah, Daing Jamil |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This project is based on fitness function determination of Unmanned Aerial Vehicle (UAV) anomaly detection in large data set. Fitness function is a solution to the issue as input and outputs how "fit" or "excellent" the answer is with regard to the problem under discussion. Based on previous research there are limited used of Particle Swarm Optimization (PSO). In this project, by using the PSO method define the fault of motor or blade by detecting it with acceleration, it is measure of how quickly speed changes with time. The measure of acceleration is expressed in units of (metres per second) per second or metres per second squared (m/s2). PSO method along with the monitoring based, can identify where exactly the fault has happened. Vibration velocity will be increase about two times from the normal velocity if the fault detected. To reduce the costing part of the Unmanned Aerial Vehicle (UAV) testing and detection of fault, the data is collected by using software in the loop with three program such as mission planner, ardupilot and flight gear. Through the simulation, that has been done it is verified by using PSO the fault occur at the motor/blade of UAV can be detected with a true positive detection rate of 76%. |
| first_indexed | 2025-11-15T03:32:25Z |
| format | Undergraduates Project Papers |
| id | ump-39010 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:32:25Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-390102023-10-25T01:36:31Z http://umpir.ump.edu.my/id/eprint/39010/ Fitness function determination of uav anomaly detection in large data set via pso Fatimah, Daing Jamil TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering This project is based on fitness function determination of Unmanned Aerial Vehicle (UAV) anomaly detection in large data set. Fitness function is a solution to the issue as input and outputs how "fit" or "excellent" the answer is with regard to the problem under discussion. Based on previous research there are limited used of Particle Swarm Optimization (PSO). In this project, by using the PSO method define the fault of motor or blade by detecting it with acceleration, it is measure of how quickly speed changes with time. The measure of acceleration is expressed in units of (metres per second) per second or metres per second squared (m/s2). PSO method along with the monitoring based, can identify where exactly the fault has happened. Vibration velocity will be increase about two times from the normal velocity if the fault detected. To reduce the costing part of the Unmanned Aerial Vehicle (UAV) testing and detection of fault, the data is collected by using software in the loop with three program such as mission planner, ardupilot and flight gear. Through the simulation, that has been done it is verified by using PSO the fault occur at the motor/blade of UAV can be detected with a true positive detection rate of 76%. 2022-02 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39010/1/EA18061_FATIMAH%20DAING%20JAMIL_THESIS%20-%20Fatimah%20Daing.pdf Fatimah, Daing Jamil (2022) Fitness function determination of uav anomaly detection in large data set via pso. College of Engineering, Universiti Malaysia Pahang. |
| spellingShingle | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Fatimah, Daing Jamil Fitness function determination of uav anomaly detection in large data set via pso |
| title | Fitness function determination of uav anomaly detection in large data set via pso |
| title_full | Fitness function determination of uav anomaly detection in large data set via pso |
| title_fullStr | Fitness function determination of uav anomaly detection in large data set via pso |
| title_full_unstemmed | Fitness function determination of uav anomaly detection in large data set via pso |
| title_short | Fitness function determination of uav anomaly detection in large data set via pso |
| title_sort | fitness function determination of uav anomaly detection in large data set via pso |
| topic | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/39010/ http://umpir.ump.edu.my/id/eprint/39010/1/EA18061_FATIMAH%20DAING%20JAMIL_THESIS%20-%20Fatimah%20Daing.pdf |