The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction

Yang, Linchao; Jia, Guozhu; Wei, Fajie; Chang, Wenbing; Li, Chen; Zhou, Shenghan

doi:10.3390/app11083448

Cited by 11 publications

(3 citation statements)

References 33 publications

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“…In addition, an external disturbance sliding mode control method was proposed for the altitude attitude system [6]. 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.…”

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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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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“…Moreover, they are employed for power line inspection and substation equipment monitoring. UAV is a complex system that integrates multiple disciplines such as electronics, control, sensors, and information, and it is usually designed with low or no redundancy [4,5]. Since there is no pilot on-site operation in the process of carrying out the task, the UAV does not have the real-time observation and response-ability of the pilot, and it will not be able to take emergency measures in time in case of failure, which leads to a higher accident rate of the UAV compared with that of the man-machine.…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

Zhou,

He,

Chen

et al. 2024

Aerospace

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The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety.

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“…With the development of Prognostics Health Management (PHM) technology, abundant onboard sensors and multisource analysis records have brought about the swift growth of operation and maintenance data of UAVs [4]. These data-driven methods, thanks to the growth of data scales, are gradually replacing the traditional Physics of Failure (PoF) methods [5,6], becoming the mainstream of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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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The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction

Cited by 11 publications

References 33 publications

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

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

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

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

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