Structural fault diagnosis of UAV based on convolutional neural network and data processing technology

Yumeng, Ma; Mustapha, Faizal; Ishak, Mohamad Ridzwan; Rahim, Sharafiz Abdul; Mustapha, Mazli

doi:10.1080/10589759.2023.2206655

Cited by 13 publications

(4 citation statements)

References 46 publications

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“…Although traditional machine learning methods have achieved good results in the field of fault diagnosis, the high complexity of UAV flight data and the huge amount of data make traditional machine learning methods insufficient in dealing with high-dimensional data, thus affecting the fault diagnosis performance. Deep learning with its excellent feature learning ability and efficient data processing ability makes it widely used in the field of fault detection and diagnosis of UAV, in which long short-term memory (LSTM) network [19,20], convolutional neural network (CNN) [21,22], gate recursive unit (GRU) [23], deep forests [24] and other deep learning algorithms have yielded fruitful results.…”

Section: Introductionmentioning

confidence: 99%

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

Huang,

Liang,

Dai

et al. 2024

Meas. Sci. Technol.

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To address the problems of external interference during unmanned aerial vehicle (UAV) flight and the low accuracy and weak generalization ability of the current single fault diagnosis model, this work proposes a weighted ensemble deep learning UAV fault diagnosis method. First, considering the differences in training methods and fault feature recognition principles of deep networks with different structures, three hybrid fault diagnosis models are constructed. These models are constructed by combining convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) and bidirectional gate recurrent unit (BiGRU). CNN is used to extract the features of the UAV flight data and the obtained feature information is fed into BiLSTM and BiGRU to explore the fault information inherent in the time series data. Then, the three hybrid fault diagnosis models are used as the individual model of the ensemble learning algorithm, and the weights of the three individual models are optimized using a random grid search algorithm to construct a UAV fault diagnosis model based on hybrid deep learning weighted ensemble, which further improves the fault diagnosis performance. Finally, it is demonstrated experimentally that the proposed hybrid deep learning weighted ensemble fault diagnosis model can effectively identify the fault of UAV with an accuracy of 99.22% and 99.62% on binary and multivariate classification, respectively, and reflects better generalization performance in the metrics of precision, recall, and F1 score.

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Section: Introductionmentioning

confidence: 99%

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

Huang,

Liang,

Dai

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In reference [11], regarding the problem whereby the UAV system cannot accurately carry out fault monitoring and diagnosis, the collected vibration signal was input into the convolutional neural network with three layers of convolution layer and pooling layer, and the accuracy of fault diagnosis reaches 97.5%. Ma [12] designed a UAV experimental acquisition system based on the vibration signal, which is used for nondestructive UAV testing and fault diagnosis methods. Four kinds of vibration signals were collected in the experiment, namely normal status and three kinds of fault status.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Liu,

Yan

et al. 2024

Processes

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Regarding the difficulty of extracting fault information in the faulty status of UAV (unmanned aerial vehicle) engines and the high time cost and large data requirement of the existing deep learning fault diagnosis algorithms with many training parameters, in this paper, a small-sample transfer learning fault diagnosis algorithm is proposed. First, vibration signals under the engine fault status are converted into a two-dimensional time-frequency map by multiple simultaneous squeezing S-transform (MSSST), which reduces the randomness of manually extracted features. Second, to address the problems of slow network model training and large data sample requirement, a transfer diagnosis strategy using the fine-tuned time-frequency map samples as the pre-training model of the ResNet-18 convolutional neural network is proposed. In addition, in order to improve the training effect of the network model, an agent model is introduced to optimize the hyperparameter network autonomously. Finally, experiments show that the algorithm proposed in this paper can obtain high classification accuracy in fault diagnosis of UAV engines compared to other commonly used methods, with a classification accuracy of faults as high as 97.1751%; in addition, we show that it maintains a very stable small-sample migratory learning capability under this condition.

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“…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. The authors of (Ma et al 2024) designed a fault data acquisition system for UAVs fault checking and diagnosis. The authors of (Du et al 2022a) proposed an approach that is interval-based sampling to reconstruct oscillatory signals and 1D CNN DL for possible mechanical failures of UAV rotor during operation.…”

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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Structural fault diagnosis of UAV based on convolutional neural network and data processing technology

Cited by 13 publications

References 46 publications

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

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

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

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