DC/DC converters are widely used as power supplies in various power supply systems. Their faults can lead to improper system operation. Building intelligent fault diagnosis models for ‘highly reliable and long-life’ DC/DC converters is challenging due to the high data acquisition costs and insufficient fault data. In this paper, the digital twin (DT) technique is utilized to provide extensive and reliable data to address the issue of insufficient data for diagnosing DC/DC converter faults. However, establishing multidisciplinary, multi-physical quantity, multi-scale, and multi-probability virtual models (VMs) can be challenging. Thus, a cloud model-based interpretable transfer model is designed to overcome the limitations of VMs in fully deducing the full-state output properties. The proposed method effectively diagnoses DC/DC converters based on DT technology by providing a more comprehensive and uniformly distributed source domain data to suit the samples in the target domain. Comparison with other algorithms shows the effectiveness of the proposed method. It has the potential to provide accurate and real-time diagnosis for DC/DC converter faults and enable timely maintenance strategies for power monitoring in equipment, such as submarine observation networks and space power supply systems.

Tailplane control system (TCS) is a key component to ensure pitch maneuverability and horizontal stability in flight control. However, due to inherent sealing, time-varying, and uncertainty, conventional control methods involve enormous challenges to guarantee optimal operation of the TCS. This article proposes a digital twin-based predictive control method, called twin predictive control (TPC), to explore tailplane optimal control under complex conditions. First, a digital twin of the TCS is established as a predictive model, and twin adaptation is set up to overcome parameter time-varying and uncertainty for supporting accurate prediction. Then, an optimization model is constructed combining tracking error and control adjustment. Based on model transformation, the optimization objective is theoretically proved convex and coupled with the active-set method to enable efficient optimization solving. Finally, the TPC method, integrating the digital twin and the optimization model, is implemented into a physical experimental system to verify the effectiveness of the proposed method. The experimental results and comparisons show that the TPC method can significantly improve tracking and antiinterference performance. Furthermore, it shows merits in excessive adjustment suppression.

Addressing the challenge of assessing the reliability of ‘long-life, high-reliability’ DC/DC analog converters in submarine observation networks and space power systems, which have limited internal observation points and insufficient valid information, and initial assessment rendered unreliable with the long-term operation of the converter. This paper proposes a novel method based on digital twin (DT) technology. The method eliminates the need for measuring internal variables, and only requires the measurement of the converter’s ripple peak-to-peak. A digital twin (DT) is constructed based on the underlying mechanism, and the parameters of the operating converter are identified through the collection of ripple peak-to-peak, which are then injected into the DT for dynamic tracking of the physical entity. By obtaining internal current and voltage of the DT and utilizing the Markov model, the reliability of the converter can be assessed. This method offers real-time reliability assessment without the need for additional measurement points. To validate the effectiveness of the proposed approach, a Superbuck converter is used as an example to demonstrate the high-fidelity of the DT through isomorphic single-sample repeatability and isomorphic multiple-sample consistency experiments.

Accurate fault diagnosis and remaining useful life (RUL) prediction are critical for health management before fatal system failures occur. Reasonable stage prediction of RUL can avoid the waste of computing power. However, the difficult-to-measure multiple metrics of real systems cause the starting and end point of RUL to be challenging to determine effectively. Based on the data distribution and digital twin technique, this paper proposes a method to collect the steady-state output signal to determine the beginning and end points of the stable equilibrium state system (SET) RUL. The combination of DT technology can accurately reproduce the actual operating state and measurable characteristics of the system, update the component degradation model by collecting the current component parameters in real-time after the starting point of RUL and inject the future parameter state of the component into DT. The DT is equivalent to a multi-indicator acquisition device, which solves the problem of the physical system’s difficulty in acquiring multiple indicators determines the remaining life of the system. Achieving a reasonable starting point and accurate endpoint remaining life prediction under the dual constraints of data and mechanism. The Superbuck converter is used as an example for experiments, and the results prove that the method can reasonably determine the system’s start and end points to predict the remaining life. The comparison of the three methods proves the superior performance of the method in this paper.

Healthy DC/DC converters in undersea observation networks are essential for ensuring that these systems operate reliably over decades. Any performance degradation or failures in the DC/DC converters can result in significant, potentially incalculable damage. To prevent such issues, continuous anomaly monitoring during operation is critical for synchronized and successful mission completion. This paper introduces a method for DC/DC converter anomaly monitoring based on the complete output signal distribution. A specialized signal collection device is integrated into the monitoring system to capture and preprocess output signals, enabling reliable data acquisition in challenging environments like undersea operations. The method leverages Gaussian Process Regression (GPR) to determine monitoring thresholds and Gaussian Mixture Modelling (GMM) to detect anomalies. The robustness of the method is validated using high-order Superbuck converters, with comparative analysis against other monitoring methods, demonstrating its superior performance. Experimental results confirm that this method effectively identifies anomalous conditions across various types and orders of DC/DC converters, regardless of signal type, ensuring versatility and reliability in critical applications.