In the process of prediction and health management (PHM) about remaining useful life (RUL) of aero-engine, the state evaluation of aero-engine by constructing health index (HI) plays a very important role. At present, in the research of aero-engine state evaluation, there are still many problems: the analysis of the impact between operating conditions and sensor measurement data is insufficient, the standard for sensor data sequence selection is not established, and the HI construction is not reasonable. Aiming at these problems, firstly, a data reconstruction method is used to standardize the sensor data sequences, so as to eliminate the coupling effect between operating conditions and sensor measurement data. Secondly, different from the screening of sensor data sequences by observing the trend manually, a monotonicity standard is proposed. Then, a HI calculation method based on weighted Hellinger distance is proposed, which can construct HI more reasonably and better characterize the health condition of aero-engine. Finally, based on the standard data set of aero-engine degradation process provided by NASA in 2008, a typical HI construction method based on linear regression mapping and a similarity-based RUL prediction method are selected for comparison to verify performance of this method. The results show that the proposed method has better evaluation performance, and the HI constructed by this method is also better in RUL prediction.

With the expansion of the cyber-physical system (CPS) application area, its importance has become more and more prominent. As one of the typical applications of CPS, the anomaly detections of mobile robots have attracted the attention of all parties. As part of the CPS, mobile robots face the problem that conventional residual-based detection methods cannot identify stealthy anomalies. The conventional residual-based detection methods mainly use the residual signal calculated from the control signal and measure output for detection, which is widely used in fault diagnosis. Still, it is difficult to be useful in deceptive stealthy anomalies purposefully imposed on mobile robots, which are designed to evade the conventional detections by tampering with measure output. Furthermore, they can control the system to deviate from the expected operations, causing degradation of control performance or even damage without being detected. Based on this, by analyzing the system model of CPS and the stealthy conditions of anomalies, the improved residual-based detection method is proposed in this paper. Moreover, three stealthy anomalies purposefully imposed on an omnidirectional mobile robot (OMR) are detected by using the conventional residual-based methods and the improved residual-based method. Finally, the experimental results show that the method proposed can effectively detect the stealthy anomalies purposefully imposed on the OMR.

The levitation system is a critical component of maglev trains, yet it is susceptible to abnormalities such as guideway settlement, fixture loosening, and component aging, which may lead to incipient failures. Timely and accurate detection of such abnormal states using sensor data is essential for implementing proactive maintenance and ensuring operational safety. To address this need, this study proposes a novel multivariate time series analysis (MTSA) method based on weighted correlation coefficients and minimum distance (WCC-MD-MTSA) for anomaly detection in levitation systems. The proposed approach leverages multi-dimensional sensor data to construct an anomaly detection index using weighted correlation coefficients, followed by a minimum-distance classifier for anomaly categorization. Furthermore, to overcome the challenge of determining sliding window sizes in MTSA, an adaptive data partitioning method based on autocorrelation length is introduced, providing a more principled and stable input configuration. A comprehensive anomaly processing framework is also established, integrating detection, classification, and maintenance guidance into a systematic workflow. The effectiveness and practicality of the proposed methods are demonstrated using real-world operational data from maglev train levitation systems.

Anomaly detection of the maglev train suspension system is crucial for the train’s safe operation and maintenance. In order to solve the complexity and diversity of abnormality modes in the suspension system, this article proposes a multilayer perceptron (MLP) anomaly detection model integrating an attention mechanism (Attention), optimized using the sparrow search algorithm (SSA), named SSA–MLP–Attention. First, a convolutional neural network (CNN) is used for data feature extraction to fully explore the anomalous information in the operation data, and combined with principal component analysis (PCA) for feature dimensionality reduction to remove the redundant information. Then, the MLP is used to construct the anomaly classification model. To optimize the model performance, SSA is used to globally optimize the key hyperparameters, avoiding the local optimum problem caused by traditional manual parameter tuning. Additionally, Attention is added to enhance the model’s sensitivity to abnormal features to focus on the most discriminative abnormal features adaptively. Finally, the proposed method is validated using operational data, with both the anomaly detection rate and $F1$ -score exceeding 98%. Compared to the baseline methods, the proposed method demonstrates superior performance in detection accuracy, stability, and anomaly identification.

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation, or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

The magnetic suspension and balance system (MSBS) is a novel balance system that enables non-contact suspension of test models in wind tunnels, eliminating mechanical interference and providing more accurate aerodynamic data. However, the development of MSBS faces the critical challenge of designing a large-gap, non-contact multi-dimensional pose measurement system (MPMS) with both high accuracy and high frequency. This paper addresses this challenge through a unified co-design framework that seamlessly integrates three key innovations: (1) a real-time recursive pose feature extraction method using dynamic threshold and hysteresis comparison, (2) a nonlinear predictive filtering algorithm for signal enhancement, and (3) a synchronous parallel hardware architecture on FPGA. This integrated approach ensures that the entire processing chain - from signal acquisition to pose output - operates efficiently at 2 kHz while maintaining high accuracy. Experimental results demonstrate that the implemented MPMS achieves accuracy of 9.77 μm and 10.08m° in 5-DOF suspension tests, meeting the stringent requirements of practical wind tunnel applications.