A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

Zheng, Ke; Jia, Guozhu; Yang, Linchao; Wang, Jiaqing

doi:10.3390/app11125410

Cited by 7 publications

(5 citation statements)

References 38 publications

(37 reference statements)

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“…A Compound Fault Labeling and Diagnosis Method is described by Zheng et al at [120]. Based on flight data and Bulit-In-Test record of the UAV, they categorize various types of faults.…”

Section: Data-based Methodsmentioning

confidence: 99%

UAV Fault Detection Methods, State-of-the-Art

Puchalski

Giernacki

2022

Drones

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The continual expansion of the range of applications for unmanned aerial vehicles (UAVs) is resulting in the development of more and more sophisticated systems. The greater the complexity of the UAV, the greater the likelihood that a component will fail. Due to the fact that drones often operate in close proximity to humans, the reliability of flying robots, which directly affects the level of safety, is becoming more important. This review article presents recent research works on fault detection on unmanned flying systems. They include papers published between January 2016 and August 2022. Web of Science and Google Scholar databases were used to search for articles. Terminology related to fault detection of unmanned aerial vehicles was used as keywords. The articles were analyzed, each paper was briefly summarized and the most important details concerning each of the described articles were summarized in the table.

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“…A Compound Fault Labeling and Diagnosis Method is described by Zheng et al at [120]. Based on flight data and Bulit-In-Test record of the UAV, they categorize various types of faults.…”

Section: Data-based Methodsmentioning

confidence: 99%

UAV Fault Detection Methods, State-of-the-Art

Puchalski

Giernacki

2022

Drones

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“…In order to determine whether the proposed two-step ensemble cost-sensitive diagnosis model (MC-LGB) can effectively play a role in the imbalanced KPG fault diagnosis compared with the diagnosis models employed on the entire UAV (the baseline GBDT and FCNN; the superb XGBoost; the extremely fast LightGBM; and the balanced modified CNN) [27], our experiment analyzed the performance of the proposed MC-LGB model in its overall diagnosis ability, total misdiagnosis cost optimization, and computing resource occupation. The performance of these classifiers was compared through the following metrics: accuracy, precision, recall, F1-score, MCC, AUC, and training time.…”

Section: Methodsmentioning

confidence: 99%

“…As an improved method of GBDT, LightGBM employs Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), known as the histogram-based algorithm, to reduce the cost of calculating the gain, to speed up training, and to occupy less memory usage [35]. It was verified on UAV test flight data that the lightning LightGBMbased diagnosis model could attain a decent performance without occupying excessive computational resources [27]. As an ensemble learning model, the LightGBM still has the great potential to be employed as a meta-classifier in complicated ensemble frameworks.…”

Section: Lightgbmmentioning

confidence: 99%

“…The new generation BIT system of UAVs, based on an expert system, PoF model, and fuzzy logic, etc., could automatically monitor most of the sensor signals or signal combinations that can cause a fault, by alerting one to or even interrupting operation instructions once the signals exceed the preset limit. Zheng utilized BIT records to label the flight data of UAVs, automatically [27]. Failure modes, effects, and criticality analysis (FMECA), a widely used methodology among the safety, reliability, and risk engineers, provide valuable information for the improvement of the safety and reliability of the process and system [28,29].…”

mentioning

confidence: 99%

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A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

et al. 2021

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In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance.

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“…Yang et al [7] used interval data to reduce the dimension of flight data and realized real-time prediction of UAV faults through a BP neural network. Zheng et al [8] proposed a composite faultmarking method based on UAV flight data and BIT recorded data and diagnosed the UAV composite fault mode by using XGBoost, LightGBM, and a modified CNN algorithm. Nguyen et al [9] proposed a fault-tolerant control method based on adaptive nonsingular fast terminal sliding mode control and neural network approximation, which could control the disturbance of four-axis aircraft.…”

Section: Literature Review 21 Data Driven Fault Analysis Methodsmentioning

confidence: 99%

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

et al. 2021

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At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults reported by UAVs generally requires experienced technicians with professional equipment. To improve the efficiency of UAV troubleshooting, this paper proposed a troubleshooting mode selection method based on SIF-SVM (Serial information fusion and support vector machine) using the text feature data from fault description records. First, Word2Vec was used in text data feature extraction. Second, in order to increase the amount of information in the modeling data, we used the information fusion method. SVM was then used to construct the classification model for troubleshooting mode selection. Finally, the effectiveness of the proposed model was verified by using the fault record data of a new fixed-wing UAV.

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A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

Cited by 7 publications

References 38 publications

UAV Fault Detection Methods, State-of-the-Art

UAV Fault Detection Methods, State-of-the-Art

A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

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