The aluminum electrolytic capacitor (AEC) is one of the most important parts of a power electronic converter. Throughout its lifespan, the equivalent series resistance (ESR) of the AEC will increase, which will affect the performance of the power electronic converter. To guarantee the reliability of a converter, it is necessary to monitor the health state of the AEC, for which one common technique is to estimate the ESR of the AEC. Equipment with a high sampling rate is usually required because converter usually works at a high frequency. This requirement will increase the cost of ESR monitoring. To address this issue, this article proposes a novel method of estimating the ESR for an AEC. The proposed method uses an innovative compressed sensing approach to acquire the ripple voltage and current. Instead of complete reconstruction of the ripple voltage and current, only the high-frequency features in the wavelet subspace are reconstructed and used to calculate the ESR. The proposed method reduces the cost of data sampling, transmission, and storage. This method is not limited by the converter circuit structure and can be applied in various environments. Simulations and experiments were carried out to verify the effectiveness of the proposed method.

DC/DC converters play an important role in electrical systems. The anomalous state of a DC/DC converter has a major impact on the operation of the back-end components and the entire electrical system. To effectively recognize an anomalous state, particularly for DC/DC converters with unknown circuit structures, an online anomaly detection method that involves statistical feature estimation using Gaussian process regression (GPR) and a genetic algorithm (GA) is proposed. In the proposed method, the normal output range is built upon the DC/DC normal output signal using GPR, and seven statistical features are considered as the estimation indexes. In the detection process, the output signal of the DC/DC converter is acquired, and the corresponding statistical features are calculated by using GA, which can effectively reduce the operation time and the calculation consumption. The working state of the DC/DC converter is distinguished according to these features. Simulation and hardware experimental results validate the practicability and effectiveness of the proposed method.

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 this paper, we propose a novel post-bond TSV test method for post-bond TSV manufacture test. The proposed method can measure the RGC parameters of TSVs using switched-capacitor circuits and calibration method and detect TSV leakage fault, open fault, high-resistance fault, and delay fault based on the measured RGC parameters. The test process has a high resolution and is robust. The fault-detection effectiveness is evaluated via HSPICE simulations. The results of the TSV capacitance measurement have a relative error within 1%, the TSV resistance measurement results have a relative error within 5%, and the TSV conductance measurement results have a relative error within 8.5%. We also perform an MC simulation to prove that even under process variation, the proposed method can obtain a TSV capacitance within an absolute error of ± 3 fF, obtain a TSV resistance with a relative error of no more than an 18.4%, and obtain a TSV conductance of less than 100 μΩ¯¹ with a relative error of no more than an 13%.

An open-circuit fault will increase input current ripple and reduce the energy transmission efficiency, which will deteriorate the inherent advantages of an interleaved boost converter. To guarantee the efficient and reliable operation of a system, it is necessary to diagnose the open-circuit fault by sensing the input current. In practical application, the sampling value of the input current will be polluted by noise and disturbance, which will weaken the robustness of fault diagnosis. To address the problem, this article proposes a robust open-circuit fault diagnosis method. By theoretically deriving the input current, it can be found that the input current contains helpful information, which can be used to identify the fault phase. The proposed method extracts this useful information by sampling the input current and holding it at specific moments. Moreover, this method integrates the sampling values to enhance robustness against noise. The fault phase can quickly be detected and located by observing and comparing the integrals during two switching periods. The method has robustness against the noise and the disturbance. Additionally, the method needs no extra sensors to sense the input current, which the control loop uses. Simulations and hardware experiments are carried to prove the practicability and the robustness of the proposed method.