Characterization and Prognosis of Multirotor Failures

Brown, Joseph; Coffey, Jesse A.; Harvey, Dustin; Thayer, Jordan M.

doi:10.1007/978-3-319-15230-1_15

Cited by 9 publications

(3 citation statements)

References 2 publications

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“…Future work is to do the fault detection automatically in real-time. Other authors exploit motor current and/or acoustics measurements [16], [17].…”

Section: Related Workmentioning

confidence: 99%

Fast Fault Detection on a Quadrotor using Onboard Sensors and a Kalman Filter Approach

van Schijndel,

Sun,

de Visser

2021

Preprint

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This paper presents a novel method for fast and robust detection of actuator failures on quadrotors. The proposed algorithm has very little model dependency. A Kalman estimator estimates a stochastic effectiveness factor for every actuator, using only onboard RPM, gyro and accelerometer measurements. Then, a hypothesis test identifies the failed actuator. This algorithm is validated online in real-time, also as part of an active fault tolerant control system. Loss of actuator effectiveness is induced by ejecting the propellers from the motors. The robustness of this algorithm is further investigated offline over a range of parameter settings by replaying real flight data containing 26 propeller ejections. The detection delays are found to be in the 30∼130 ms range, without missed detections or false alarms occurring.

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“…Future work is to do the fault detection automatically in real-time. Other authors exploit motor current and/or acoustics measurements [16], [17].…”

Section: Related Workmentioning

confidence: 99%

Fast Fault Detection on a Quadrotor using Onboard Sensors and a Kalman Filter Approach

van Schijndel,

Sun,

de Visser

2021

Preprint

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“…Reliability Centered Maintenance (RCM) strategies include a Failure Mode, Effects and Criticality Analysis (FMECA). Once failure modes are identified and criticality classified, maintenance tasks to be undertaken are established to avoid faults consequences [10] . When predictive maintenance is technically possible and is economically worth it, compared to preventive and corrective ones, it is applied.…”

Section: Related Workmentioning

confidence: 99%

Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF-based score

et al. 2017

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Today, failure modes characterization and early detection is a key issue in complex assets. This is due to the negative impact of corrective operations and the conservative strategies usually put in practice, focused on preventive maintenance. In this paper anomaly detection issue is addressed in new monitoring sensor data by characterizing and modeling operational behaviors. The learning framework is performed on the basis of a machine learning approach that combines constrained K-means clustering for outlier detection and fuzzy modeling of distances to normality. A final score is also calculated over time, considering the membership degree to resulting fuzzy sets and a local outlier factor. Proposed solution is deployed in a CBM+ platform for online monitoring of the assets. In order to show the validity of the approach, experiments have been conducted on real operational faults in an auxiliary marine diesel engine. Experimental results show a fully comprehensive yet accurate prognostics approach, improving detection capabilities and knowledge management. The performance achieved is quite high (precision, sensitivity and specificity above 93% and κ = 0. 93), even more so given that a very small percentage of real faults are present in data.

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“…In recent years, studies have been conducted on the use of embedded [7,8] or surface-mounted sensing elements on the structure of the UAS to observe and characterize structural failures. In another work [9], failures on the aircraft structure, including the propeller, were studied by utilizing external accelerometers mounted on the arm(s) of the quadcopter, in a static configuration.…”

Section: Introductionmentioning

confidence: 99%

Flights of a Multirotor UAS with Structural Faults: Failures on Composite Propeller(s)

Gururajan

Mitchell

Ebel

2019

Data

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Data acquired from several flights of a custom-fabricated Hexacopter Unmanned Aerial System (UAS) with composite structure (carbon fiber arms and central hub) and composite (carbon fiber) propellers are described in this article. The Hexacopter was assembled from a commercially available kit (Tarot 690) and flown in manual and autonomous modes. Takeoffs and landings were under manual control and the bulk of the flight tests was conducted with the Hexacopter in a “position hold” mode. All flights were flown within the UAS flight cage at Parks College of Engineering, Aviation and Technology at Saint Louis University for approximately 5 min each. Several failure conditions (different types, artificially induced) on the composite (carbon fiber) propellers were tested, including failures on up to two propellers. The dataset described in this article contains flight data from the onboard flight controller (Pixhawk) as well as three accelerometers, each with three axes, mounted on the arms of the Hexacopter UAS. The data are included as supplemental material.

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Characterization and Prognosis of Multirotor Failures

Cited by 9 publications

References 2 publications

Fast Fault Detection on a Quadrotor using Onboard Sensors and a Kalman Filter Approach

Fast Fault Detection on a Quadrotor using Onboard Sensors and a Kalman Filter Approach

Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF-based score

Flights of a Multirotor UAS with Structural Faults: Failures on Composite Propeller(s)

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