Predictive Maintenance with Neural Network Approach for UAV Propulsion Systems Monitoring

Zahra, Nabila; Buldan, Rasis Syauqi; Nazaruddin, Yul Y.; Widyotriatmo, Augie

doi:10.23919/acc50511.2021.9482858

Cited by 10 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although unmanned aircraft systems (UAS) have been used in military operations for a long time, they have recently become more appealing in civil aviation such as commercial and healthcare applications that are subjected to fly across urban areas to deliver packages or medical products. [1][2][3] With the increase in civilian use of UAS, there has been a significant increase in UAS accident rates, with a recorded accident rate of 100 times more than manned aircraft. [4][5][6] It was found that 33 to 67% of accidents involving UAS were caused by failures associated with mechanical or electrical components.…”

Section: Introductionmentioning

confidence: 99%

A data-driven predictive maintenance model to estimate RUL in a multi-rotor UAS

Özkat

Bektaş

Nielsen

et al. 2023

International Journal of Micro Air Vehicles

View full text Add to dashboard Cite

Unmanned Aircraft Systems (UAS) has become widespread over the last decade in various commercial or personal applications such as entertainment, transportation, search and rescue. However, this emerging growth has led to new challenges mainly associated with unintentional incidents or accidents that can cause serious damage to civilians or disrupt manned aerial activities. Machine failure makes up almost 50% of the cause of accidents, with almost 40% of the failures caused in the propulsion systems. To prevent accidents related to mechanical failure, it is important to accurately estimate the Remaining Useful Life (RUL) of a UAS. This paper proposes a new method to estimate RUL using vibration data collected from a multi-rotor UAS. A novel feature called mean peak frequency, which is the average of peak frequencies obtained at each time instance, is proposed to assess degradation. The Long Short-Term Memory (LSTM) is employed to forecast the subsequent 5 mean peak frequency values using the last 7 computed values as input. If one of the estimated values exceeds the predefined 50 Hz threshold, the time from the estimation until the threshold is exceeded is calculated as the RUL. The estimated mean peak frequency values are compared with the actual values to analyze the success of the estimation. For the 1st, 2nd, and 3rd replications, RUL results are 4 s, 10 s, and 10 s, and root mean square error (RMSE) values are 3.7142 Hz, 1.4831 Hz, and 1.3455 Hz, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

A data-driven predictive maintenance model to estimate RUL in a multi-rotor UAS

Özkat

Bektaş

Nielsen

et al. 2023

International Journal of Micro Air Vehicles

View full text Add to dashboard Cite

show abstract

“…(2018a) used the finite element method to determine mechanical stress on a micro UAV body for selection maintenance actions. Also, Nabila et al. (2021) considered UAV propulsion and defined predictive maintenance via a neural network approach.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Martinetti et al (2018a) used the finite element method to determine mechanical stress on a micro UAV body for selection maintenance actions. Also, Nabila et al (2021) considered UAV propulsion and defined predictive maintenance via a neural network approach. When we deal with a complex system, this approach may increase maintenance times and reduce availability.…”

mentioning

confidence: 99%

RBM-MCDM framework for optimization of maintenance and inspection intervals of small unmanned aircrafts

Farsi

2022

JQME

View full text Add to dashboard Cite

PurposeUnmanned aircraft applications are quickly expanded in different fields. These systems are complex that include several subsystems with different types of technologies. Maintenance and inspection planning is necessary to obtain optimal performance and effectiveness. The failure rate in these systems is more than commercial and manned aircraft since they are usually cheaper. But maintenance and operation planning are difficult because we deal with a system that has multi-components, multi-failure models, and different dependencies between subsystems without any advanced health monitoring system. In this paper, this matter is considered and a framework to determine optimal maintenance and inspection plan for this type of system is proposed to improve system reliability and availability. The new criteria according to this field are proposed.Design/methodology/approachMaintenance of unmanned systems influences their readiness; also, according to the complexity of the system and different types of components, maintenance programming is a vital requirement. The plan should consider several criteria and disciplines; thus, multicriteria decision approaches may be useful. On another side, the reliability and safety of unmanned aircraft are the most important requirements in the design and operation phases. The authors consider these parameters and develop a framework based on risk-based maintenance to overcome the problems for unmanned systems. This framework consists of two stages: at the first stage, the critical components and failure modes are determined by FMEA, and in the second stage, the priority of maintenance tasks is determined by a fuzzy multicriteria weighted decision system. In this study, fourteen criteria with different levels of importance are developed and proposed to find the best plan for maintenance and inspection intervals. These criteria have been extracted from the literature review, the author's experience, and expert opinions.FindingsA novel framework for risk-based maintenance has been proposed. Risk determination and risk criteria are the most important factors in this framework. Risks are determined by FMEA, and new criteria are proposed that are used for decision-making. These criteria are proposed based on practical experience and experts' opinions for the maintenance process in the aeronautic industry. These are evaluated by industrial cases, and this framework capability has been demonstrated.Research limitations/implicationsThe proposed framework and criteria for small unmanned aircraft have been developed based on a practical point of view and expert opinion. Thus for implementation in other aeronautic industries, the framework may need a minor modification.Practical implicationsTwo important subsystems of an unmanned aircraft have been studied, and the capabilities of this method have been presented.Originality/valueThis research is original work to determine a maintenance program for unmanned aircraft that their application has rapidly grown up. Practical and design parameters have been considered in this work.

show abstract

“…Unlike model-based diagnosis systems, data-driven designs do not employ complex mathematical models to represent the dynamics of the UAV. As such, they can be more efficient in diagnosing onboard faults, and hence, many researchers have favored them [12]- [16]. Some studies use parametric sensors to measure vibration, temperature, current, and voltage data and then use the collected data for fault identification [12], [14], [16].…”

Section: Introductionmentioning

confidence: 99%

“…As such, they can be more efficient in diagnosing onboard faults, and hence, many researchers have favored them [12]- [16]. Some studies use parametric sensors to measure vibration, temperature, current, and voltage data and then use the collected data for fault identification [12], [14], [16]. Another study proposes feature extraction from airframe vibration signal data and uses it to train neural networks through timefrequency wavelet packet decomposition [17].…”

Section: Introductionmentioning

confidence: 99%

Hardware-in-the-loop Simulation for Quadrotor Fault Diagnosis Enhancing Airworthiness using OS-Fuzzy-ELM

Thanaraj

Sai

et al. 2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

View full text Add to dashboard Cite

With the rising adoption of multi-rotor UAVs, it has become ever more crucial that their airworthiness is ensured, especially for hobby-grade UAVs. For some UAVs, their onboard components might not be as reliable as required and as such, faults can occur during flight operations and may develop to cause catastrophe. Hence, these faults need to be accurately diagnosed and quickly mitigated. This paper presents a fault diagnosis model for a quadrotor subjected to partial actuator faults. Flight simulations, with actuator and GPS sensor fault injections, are performed on a hardware-in-the-loop experimental setup to gather flight data consisting of multiple sensors. Based on this data, a preliminary controllability threshold analysis is conducted for the quadrotor. After that, a fault diagnosis model using an online sequential fuzzy-extreme learning machine (OS-Fuzzy-ELM) is trained to locate the actuator faults on the quadrotor UAV. The trained model presents an average testing accuracy and macro-averaged F1 score of 80.2% and 78.0%. A subsequent study to isolate sensor and actuator faults presents the testing accuracy and macroaveraged F1 score to be 1.95% and 1.21%, marginally better than a fault diagnosis model based on a single-layer feedforward network

show abstract

Predictive Maintenance with Neural Network Approach for UAV Propulsion Systems Monitoring

Cited by 10 publications

References 16 publications

A data-driven predictive maintenance model to estimate RUL in a multi-rotor UAS

A data-driven predictive maintenance model to estimate RUL in a multi-rotor UAS

RBM-MCDM framework for optimization of maintenance and inspection intervals of small unmanned aircrafts

Hardware-in-the-loop Simulation for Quadrotor Fault Diagnosis Enhancing Airworthiness using OS-Fuzzy-ELM

Contact Info

Product

Resources

About