Anomaly detection is a crucial matter to ensure the spacecraft stability. During the spacecraft operation, sensors and controllers generate a large volume of multidimensional time series telemetry data with long periodicity, and one key point to detect the anomaly inside the spacecraft timely and precisely is to extract essential features from the sheer amount of telemetry data. However, great challenges exist owing to the complex coupling relationships and the temporal characteristics inside the telemetry data. To address this issue, we propose a novel approach called maximum information coefficient attention graph network (MAG). The basic frame is a graph neural network, which utilizes embedding vectors to describe the intrinsic properties of each dimension, correlation analysis to investigate long-term dependencies, an attention mechanism to determine short-term interactions among dimensions, and long short term memory (LSTM) to extract temporal features. The fusion of these modules through a graph neural network results in the construction of the MAG model, allowing for a comprehensive analysis of complex variable relationships and temporal characteristics leading to successful detection of various types of anomalies. Since telemetry data has heterogeneous characteristics, we adapt the loss function and design an unsupervised anomaly scoring method suitable for MAG. To verify the effectiveness of the proposed algorithm, we conducted experiments using two publicly and two new available spacecraft telemetry datasets, and the results demonstrate that our algorithm is more efficient and accurate in detecting spacecraft data anomalies than several other state-of-the-art methods.

Spacecraft anomaly detection which could find anomalies in the telemetry or test data in advance and avoid the occurrence of catastrophic failures after taking corresponding measures has elicited the attention of researchers both in academia and aerospace industry. Current spacecraft anomaly detection systems require costly knowledge and human expertise to identify a true anomaly. Moreover, some new problems and challenges such as large volume of test data, imbalanced data distribution and the scarcity of faulty labeled samples have emerged. In this work, we propose an unsupervised anomaly detection algorithm combining Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) and Extreme Value Theory (EVT). First, we develop a two-layer ensemble learning based predictor framework which stacks three GRU-based networks with different architectures to learn and capture the normal behavior of multiple channels of data. Then, the prediction errors are calculated and smoothed using Exponentially Weighted Moving Average (EWMA) algorithm. Next, we propose a detection rule setting anomaly threshold automatically through EVT which does not assume any parent distribution on the prediction errors. To the best of our knowledge, it is the first attempt that stacked GRU-based predictors with EVT has been employed into the spacecraft anomaly detection. Through extensive experiments conducted on public datasets as well as real data sampled from a launch vehicle, we show that the proposed detection algorithm is superior to other state-of-the-art anomaly detection approaches in terms of model performance and robustness.

The aero-engine is the heart of an aircraft; its performance deteriorates rapidly due to the high temperature and high-pressure environment during flights. It is necessary to predict the remaining useful life (RUL) to improve the reliability of aero-engines and provide security for reliable flights. In previous flights, the sensors collected a lot of performance parameter data and formed a database regarding the aero-engine degradation process. These performance parameters cannot reflect the degradation process directly. In this paper, fuzzy clustering is applied to divide the degradation stages of the aero-engine, construct the health indicator, and describe the degradation process. Time-series decomposition modeling is applied to predict the degradation process of the health indicator. Based on the idea of similarity comparison, the RUL is predicted by comparing the similarity of time series through example learning. The method is verified and analyzed on the dataset published by National Aeronautics and Space Administration (NASA), and the mean square error (MSE) is 528. The result is better than the comparative method.

Remaining useful life (RUL) prediction is the main approach to guarantee the reliability of lithium-ion batteries. This paper proposes an interpretable hybrid method to predict the RUL distribution with changeable form. The method integrates prior knowledge from the statistical model and regular patterns learned from monitoring data based on the data-driven model. The predicted compound distribution provides more information compared to point estimation and distribution with fixed form. The general hybrid framework contains a component learner, a fusion model with a stacking strategy, and a prognostic distribution algorithm with adaptive sampling weights. The stacking fusion model is implemented by a one-dimensional convolution neural network. The sampling weights are estimated by optimal estimation. The statistical model describes the individual capacity degradation path based on the Wiener process. The data-driven model learns the degradation process from historical data based on convolutional gated recurrent neural network (CNN-GRU) and Monte Carlo dropout simulation. The comparative experiments between the proposed method and existing methods were carried out. The experiment results show that the proposed hybrid method performs well.

An Inertial Measurement Unit (IMU) is a significant component of a spacecraft, and its fault diagnosis results directly affect the spacecraft’s stability and reliability. In recent years, deep learning-based fault diagnosis methods have made great achievements; however, some problems such as how to extract effective fault features and how to promote the training process of deep networks are still to be solved. Therefore, in this study, a novel intelligent fault diagnosis approach combining a deep residual convolutional neural network (CNN) and a data preprocessing algorithm is proposed. Firstly, the short-time Fourier transform (STFT) is adopted to transform the raw time domain data into time–frequency images so the useful information and features can be extracted. Then, the Z-score normalization and data augmentation strategies are both explored and exploited to facilitate the training of the subsequent deep model. Furthermore, a modified CNN-based deep diagnosis model, which utilizes the Parameter Rectified Linear Unit (PReLU) as activation functions and residual blocks, automatically learns fault features and classifies fault types. Finally, the experiment’s results indicate that the proposed method has good fault features’ extraction ability and performs better than other baseline models in terms of classification accuracy.