No abstract

In the process of Unmanned Aerial Vehicle (UAV) flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by Built-In-Test (BIT) equipment. At present, there still lacks a suitable automatic labeling approach for UAV flight data, effectively utilizing the information of the BIT record. The performance of the originally employed flight data-driven fault diagnosis models based on machine learning needs to be improved as well. A compound fault labeling and diagnosis method based on actual flight data and the BIT record of the UAV during flight test phase is proposed, through labeling the flight data with compound fault modes corresponding to concurrent single faults recorded by the BIT system, and upgrading the original diagnosis model based on Gradient Boosting Decision Tree (GBDT) and Fully Convolutional Network (FCNN), to eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and modified Convolutional Neural Network (CNN). The experimental results based on actual test flight data show that the proposed method could effectively label the flight data and obtain a significant improvement in diagnostic performance, appearing to be practical in the UAV test flight process.

Traffic flow prediction is one of the basic, key problems with developing an intelligent transportation system since accurate and timely traffic flow prediction can provide information support and decision support for traffic control and guidance. However, due to the complex characteristics of traffic information, it is still a challenging task. This paper proposes a novel hybrid deep learning model for short-term traffic flow prediction by considering the inherent features of traffic data. The proposed model consists of three components: the recent, daily and weekly components. The recent component is integrated with an improved graph convolutional network (GCN) and bi-directional LSTM (Bi-LSTM). It is designed to capture spatiotemporal features. The remaining two components are built by multi-layer Bi-LSTM. They are developed to extract the periodic features. The proposed model focus on the important information by using an attention mechanism. We tested the performance of our model with a real-world traffic dataset and the experimental results indicate that our model has better prediction performance than those developed previously.

This paper proposes a complete-information-based principal component analysis (CIPCA)-back-propagation neural network (BPNN)_ fault prediction method using real unmanned aerial vehicle (UAV) flight data. Unmanned aerial vehicles are widely used in commercial and industrial fields. With the development of UAV technology, it is imperative to diagnose and predict UAV faults and improve their safety and reliability. The data-driven fault prediction method provides a basis for UAV fault prediction. A UAV is a typical complex system. Its flight data is a kind of typical high-dimensional large sample dataset, and traditional methods cannot meet the requirements of data compression and dimensionality reduction at the same time. The method used interval data to compress UAV flight data, used CIPCA to reduce the dimensionality of the compressed data, and then used a back propagation (BP) neural network to predict UAV failure. Experimental results show that the CIPCA-BPNN method had obvious advantages over the traditional principal component analysis (PCA)-BPNN method and could accurately predict a failure about 9 s before the UAV failure occurred.

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.

Water is the source of life, and in recent years, with the progress in technology, water quality data have shown explosive growth; how to use the massive amounts of data for water quality prediction services has become a new opportunity and challenge. In this paper, we use the surface water quality data of an area in Beijing collected and compiled by Zhongguancun International Medical Laboratory Certification Co., Ltd. (Beijing, China). On this basis, we decompose the original water quality indicator data series into two series in terms of trend and fluctuation; for the characteristics of the decomposed series data, we use the traditional time series prediction method to model the trend term, introduce the deep learning method to interpret the fluctuation term, and fuse the final prediction results. Compared with other models, our proposed integrated Wavelet decomposition, Autoregressive Integrated Moving Average (ARIMA) and Gated Recurrent Unit (GRU) model, which is abbreviated as the W-ARIMA-GRU model, has better prediction accuracy, stability, and robustness for three conventional water quality indicators. At the same time, this paper uses the ensemble learning model LightGBM for the prediction of water quality evaluation level, and the accuracy and F1-score reached 97.5% and 97.8%, respectively, showing very strong performance. This paper establishes a set of effective water quality prediction frameworks that can be used for timely water quality prediction and to provide a theoretical model and scientific and reasonable analysis reference for the relevant departments for advanced control.

This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as the voltage of the generator, angle of attack, and position of the rudder surface. A UAV is a typical complex system, and its flight data are typical high-dimensional large sample data sets. In practical applications such as UAV fault detection, the fault data only appear in a small part of the data sets. In this study, representation learning is used to extract the normal features of the flight data and reduce the dimensions of the data. The normal data are used for the training of the Auto-Encoder, and the reconstruction loss is used as the criterion for fault detection. An Improved Auto-Encoder suitable for UAV Flight Data Sets is proposed in this paper. In the Auto-Encoder, we use wavelet analysis to extract the low-frequency signals with different frequencies from the flight data. The Auto-Encoder is used for the feature extraction and reconstruction of the low-frequency signals with different frequencies. To improve the effectiveness of the fault localization at inference, we develop a new fault factor location model, which is based on the reconstruction loss of the Auto-Encoder and edge detection operator. The UAV Flight Data Sets are used for hard-landing detection, and an average accuracy of 91.01% is obtained. Compared with other models, the results suggest that the developed Self-supervised Fault Detection Model for UAVs has better accuracy. Concluding this study, an explanation is provided concerning the proposed model’s good results.

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.