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.

In the actual fault diagnosis process of an analog circuit, there is often a problem due to the lack of fault samples, leading to the low-accuracy of diagnostic models. Therefore, using positive samples that are easy to obtain to establish diagnostic models became a research hotspot in the field of analog circuit fault diagnosis. This paper proposes a method based on Support Vector Data Description (SVDD) and Dempster–Shafer evidence theory (D–S evidence theory) for fault diagnosis of modular analog circuit. Firstly, the principle of circuit module partition is proposed to divide the analog circuit under test, and the output port of each module is selected as test point. Secondly, the paper extracts the feature of the time-domain and frequency-domain output signals of the circuit module through Principal Component Analysis (PCA). Thirdly, four state detection models based on SVDD are established to judge the working state of each circuit module, including TSG, TSP, FSG, and FSP state detection model. Finally, the D–S theory is introduced to integrate the test results of each model for locating fault circuit module. To verify the effectiveness of the proposed method, the dual bandpass filter circuit is selected for simulation and hardware experiment. The results show that the proposed method can locate the analog fault effectively and has a higher diagnosis accuracy.

This article presents a novel current-balancing control method using virtual phase current for the interleaved boost converter. By theoretical derivation, it can be found that the output voltage contains useful information used to calculate the ratio between phase currents. The proposed method uses the ratio and the total input current to generate the virtual phase current. Once the virtual phase current is achieved, the phase current can be autotuned to balance due to the same reference from the voltage regulation control loop. Despite the N number of the phase, only a current sensor is needed. Moreover, instead of the input current details, only effective values are required, which will reduce sampling costs. A limiter is added to reduce the impact of the current balance on the stability and transient response of the voltage regulation control loop. It is also beneficial to improve the robustness against noise and external disturbances. The proposed method is easy to implement in the existing controller, including the double closed-loop controllers. The simulation and experimental results demonstrate the effectiveness and practicability.

Power factor correction (PFC) converters are widely used in power systems. The anomalous state of a PFC converter can develop into the complete or partial loss of the electric system. In some industrial applications, such as wind power generation and photovoltaic power generation, there is a fluctuation in the input voltage of the PFC converter, which brings great obstacles in anomaly detection. In order to effectively recognize an anomalous state, especially when the input voltage is unstable, a statistical feature-based anomaly detection method using canonical correlation analysis (CCA) is proposed. Firstly, statistical features are used to enhance the difference between the normal state and anomalous state. Then, the proposed anomaly detection method focuses on the correlation between the input voltage and the output voltage, so even if there is a fluctuation in the input voltage, the anomaly states can be detected precisely. The experimental results show the effectiveness of the method.

The interleaved DC-DC converter is widely used for its low current ripple and voltage ripple, alongside high energy conversion efficiency. However, open-circuit faults (OCFs) will increase current and voltage ripple while reduce energy conversion efficiency. Therefore, the OCF identification is very significant. Most current fault diagnosis methods typically depend on current sensors to obtain current information, elevating diagnostic costs. Consequently, this paper introduces a cost-effective, sensorless OCF identification method using the step change in output voltage at specific sampling moments in the interleaved boost converter. It is proved that the ratio of output voltage step value serves as an indicator of the phase current relationships. Therefore, the output voltage is only used to locate the open-circuit fault, avoiding any current sensor. Both simulation and hardware experiments verify the effectiveness and practicality of the proposed method.