Research on a Small-Sample Fault Diagnosis Method for UAV Engines Based on an MSSST and ACS-BPNN Optimized Deep Convolutional Network

Li, Siyu; Liu, Zichang; Yan, Yunbin; Han, Kai; Han, Yueming; Miao, Xinyu; Cheng, Zhonghua; Ma, Shifei

doi:10.3390/pr12020367

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…The authors of (Huang et al 2024) considered the differences in fault diagnosis principles of different network structures and proposed an integrated DL method for UAV fault diagnosis. The authors of (Li et al 2024) proposed a fault diagnosis algorithm based on small-sample transfer learning, which converts the dimensions of fault signals through multiple simultaneous compression transformations. In a recent study (Wang et al 2022), a novel Adam optimizer with a rate of exponent sign learning was introduced to regulate the iterative direction and step of the CNN approach.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective UAV fault diagnosis framework based on attention joint multi-spatial shared knowledge

An,

Li,

Zhang

et al. 2024

Preprint

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In recent years, unmanned aerial vehicles (UAVs) have had excellent performance in various fields, but their frequent component faults often lead to damages and serious accidents, so it is crucial to carry out timely fault diagnosis for them. Deep learning is widely used in the field of UAV fault diagnosis due to its superior feature extraction capability, but the increasing complexity of UAV faults and the scarcity of data have limited the development of deep learning in this field. To address the above problems, this paper proposed an Attention-based Joint Multi-Spatial Shared Knowledge Network (A-MSKN) for multi-objective fault diagnosis of UAVs under small samples. A-MSKN considers both complementary relationships between different tasks and intra-task dependencies within the same task for individual fault samples in different time intervals. Firstly, a single fault sample is divided into multiple sub-samples based on different time slices, and different sub-samples are coded to obtain different feature sub-spaces. Then, a sharing unit based on attention is designed to share not only the different feature subspaces within a task but also the features related between different tasks in a more fully shared way, to obtain more fault information for fault diagnosis under small samples. Finally, the effectiveness of the A-MSKN in the case of small samples was verified by testing it on real faulty flight data.

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Section: Introductionmentioning

confidence: 99%

A multi-objective UAV fault diagnosis framework based on attention joint multi-spatial shared knowledge

An,

Li,

Zhang

et al. 2024

Preprint

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Fault-tolerant control of nonlinear cluster system for fixed-wing UAV piston engine faults based on hierarchical architecture

Li,

Zhang

et al. 2025

Aerospace Science and Technology

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Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation

Berghout,

Benbouzid

2024

Machines

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Drones are a promising technology performing various functions, ranging from aerial photography to emergency response, requiring swift fault diagnosis methods to sustain operational continuity and minimise downtime. This optimises resources, reduces maintenance costs, and boosts mission success rates. Among these methods, traditional approaches such as visual inspection or manual testing have long been utilised. However, in recent years, data representation methods, such as deep learning systems, have achieved significant success. These methods learn patterns and relationships, enhancing fault diagnosis, but also face challenges with data complexity, uncertainties, and modelling complexities. This paper tackles these specific challenges by introducing an efficient representation learning method denoted Multiverse Augmented Recurrent Expansion (MVA-REX), allowing for an iterative understanding of both learning representations and model behaviours and gaining a better understanding of data dependencies. Additionally, this approach involves Uncertainty Bayesian Optimisation (UBO) under Extreme Learning Machine (ELM), a lighter neural network training tool, to tackle both uncertainties in data and reduce modelling complexities. Three main realistic datasets recorded based on acoustic emissions are involved in tackling propeller and motor failures in drones under realistic conditions. The UBO-MVA Extreme REX (UBO-MVA-EREX) is evaluated under many, error metrics, confusion matrix metrics, computational cost metrics, and uncertainty quantification based on both confidence and prediction interval features. Application compared to the well-known long-short term memory (LSTM), under Bayesian optimisation of the approximation error, demonstrates performances, certainty, and cost efficiency of the proposed scheme. More specifically, the accuracy obtained by UBO-MVA-EREX, ~0.9960, exceeds the accuracy of LSTM, ~0.9158, by ~8.75%. Besides, the search time for UBO-MVA-EREX is ~0.0912 s, which is ~98.15% faster than LSTM, ~4.9287 s, making it highly applicable for such challenging tasks of fault diagnosis-based acoustic emission signals of drones.

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Research on a Small-Sample Fault Diagnosis Method for UAV Engines Based on an MSSST and ACS-BPNN Optimized Deep Convolutional Network

Cited by 3 publications

References 25 publications

A multi-objective UAV fault diagnosis framework based on attention joint multi-spatial shared knowledge

A multi-objective UAV fault diagnosis framework based on attention joint multi-spatial shared knowledge

Fault-tolerant control of nonlinear cluster system for fixed-wing UAV piston engine faults based on hierarchical architecture

Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation

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