Practically inaccessible precise probability distribution for disturbance poses significant challenges to stochastic active fault detection (AFD) in achieving satisfactory detection accuracy. In this paper, without making specific distribution assumption on disturbance, a distributionally robust data-driven approach is proposed to AFD for stochastic linear dynamic systems. On the basis of constructing a data-driven stable kernel representation-based residual generator, the distributional uncertainty of disturbance is characterized by the mean-covariance-based ambiguity set of residual both for the fault-free and faulty cases. To minimize the energy of input while guarantee tolerable false alarm rate (FAR) and missed detection rate (MDR), the design of AFD system is formulated as an optimization problem subject to distributionally robust chance constraints (DRCCs). By bridging the DRCCs with deterministic constraints in the probabilistic context, the targeting optimization problem is then converted into a generalized eigenvalue-eigenvector problem, by solving which analytical solutions of the input and separating hyperplane for online detection are derived. Hence, the developed AFD system can not only ensure the FAR and MDR criteria not exceeding predefined levels, but also improve the robustness of the system against distributional uncertainties of disturbance. Besides, a batch-wise realization algorithm is developed for continuous online fault detection. A simulation study based on a four-tank system is demonstrated to validate the effectiveness of the proposed approach.

Track defect detection is crucial for ensuring train operation safety and maintaining railway infrastructure integrity. To address the problems of missed detection, inaccurate positioning, and insufficient ability to detect small-scale objects in traditional track defect detection, a track defect detection network (DSO-YOLO) based on improved YOLOv5s is proposed. This method employs a decoupling head and a small-object detection layer due to the YOLOv5s, and adopts the full-dimensional dynamic convolution module ODConv to improve object detection performance. First, the original coupled header is replaced by a decoupled one and the generalizability of Yolov5s is improved by a learning process that separates the target position and classification data. Second, the new small target detection layer expands the feature mapping from three groups to four groups; a better multiscale detection mechanism is introduced to handle targets of different sizes. Finally, ODConv is introduced into the neck structure of YOLOv5s, and a 4-D attention mechanism is adopted to accurately locate the track defect feature regions and refine the local fine-grained features for solving the problem of illumination influence as well as the overlap of defect regions. The experimental consequents show that the mean average precision of the improved model is 98.6%, surpassing YOLOv5s by 3.7%. The suggested model demonstrates higher accuracy in detecting various track defects within complex environments.

Based on a double-layer permanent magnet rotor flux adjustable permanent magnet coupler (PMC), this paper designs a slotted conductor rotor to replace the solid conductor ring rotor to improve the torque density. Firstly, the working principle of the PMC with slotted conductor rotor are introduced. Then the electromagnetic characteristics are analyzed, including the statice filed, transient field and speed regulation performance. And to illustrate its superiority, the comparison with the structure of solid conductor ring rotor is also analyzed. The results indicate that the torque and the speed adjustment range obtain improved. Finally, the effects of the parameters of structure on the mechanical characteristic are studied, including the number of slots in conductor rotor, the material of conductor, the material of PMs, and the diameter of slots, respectively. Through the research, a reference for optimizing the PMC with this structure is provided.

This research explores the formulation of state estimators for two-dimensional discrete systems grounded in the Fornasini-Marchesini second model, while also regarding the effects of bounded disturbances. To enhance the estimator's robustness, a non-fragile design scheme is introduced, effectively managing variations in the estimator gain. Simultaneously, the adoption of event-triggered communication mechanisms seeks to alleviate the transmission load on the network. To overcome the design challenges associated with non-fragile estimators impacted by bounded disturbances and event-triggered errors, a novel definition of quadratic boundedness is introduced. This approach leverages Lyapunov functions and the properties of invariant sets specific to two-dimensional systems. Utilizing this definition of quadratic boundedness, sufficient conditions for the presence of quadratically stable estimators have been identified, and the upper limit of the estimation error has been established. Grounded in this foundation, a collaborative design methodology is proposed for both the two-dimensional discrete system estimator and the event generator. In addition, the desired estimator gain matrix is obtained using convex optimization techniques.

Precisely forecasting the efficiency of lithium batteries is crucial for addressing consumer worries regarding the safety and dependability of electric vehicles. However, existing research primarily focuses on a single degradation characteristic of the battery, neglecting the multiple degradation features present in real-world operation. To more effectively utilize the various degradation information of batteries, we propose a time series prediction approach based on a Transformer to forecast the remaining useful life of the battery. The proposed Transformer architecture comprises a sampling encoding layer and a temporal variable block. Initially, the battery data undergoes time series processing; the sampling encoding layer extracts degradation features from the sensor recordings and incorporates positional information. Subsequently, in the temporal variable block, the time step encoding layer and the variable encoding layer work in parallel to extract dynamic temporal information and degradation features. hey focus on the information of different features within the feature vector, capturing the correlations between these features through a multi head self-attention mechanism, and determining the relative significance of each feature in forecasting the current time step. These elements are then integrated to produce a feature vector. Finally, the feature vector is passed to a decoder based on a multi-layer perceptron to compute the prediction outcomes. The experimental outcomes demonstrate that, on the Center for Advanced Life Cycle Engineering battery dataset, the proposed method surpasses existing approaches in forecasting the remaining useful life of batteries.

Under spatially point measurements (SPMs), this paper introduces a fault-tolerant event-triggered sampled-data (FETSD) control for the output synchronization of reaction-diffusion neural networks (RDNNs). Considering the possible actuator failure and to reduce the communication burden, a FETSD control method presented by linear matrix inequalities (LMIs) conditions is designed according to an appropriate Lyapunov functional (LF) and inequality techniques, which can guarantee the exponential stability of the synchronization error system. Lastly, one numerical example is presented to illustrate the effectiveness of the designed strategy,

Group-sparse mode decomposition (GSMD) is an efficient signal decomposition algorithm for separating harmonic signals, but fails to split modes for periodic pulse signals. In order to address the limitations of the GSMD algorithm in dealing with periodic pulse signals, this study proposes two improved GSMD methods, namely adaptive adjusted bandwidth sparse mode decomposition (AABSMD) and adaptive Gaussian window sparse mode decomposition (AGWSMD). The AABSMD method utilizes an iterative least-squares curve fitting approach to plot the energy spectrum and adjusts the filter using a –3 dB bandwidth, which avoids unreasonable bandwidth estimation. The AGWSMD method employs a segmented quantile regression method to fit the energy spectrum and utilizes a Gaussian window as a filter to extract the signal, which selects energy entropy as the parameter to optimize the Gaussian function. Both methods overcome the defects of splitting periodic pulse signals in GSMD and provide more accurate determination of the number of modes, resulting in a well-performed decomposition. In addition, the AGWSMD method exhibits outstanding reconstructive performance and high efficiency. Numerical and experimental results indicate the effectiveness and superiority of the proposed methods, which can be successfully applied to fault diagnosis of rotating machineries.

This article presents some new improvements to the relevant constrained predictive fault-tolerant tracking control (FTTC) methods through embedding optimal preview regulation and reference governance. The main novelty of such a strategy is that some valuable information of finite future references can be adequately scheduled to optimize the robust tracking performance and significantly enlarge the fault-tolerant admissible region. In order to better describe the wide applicability of the proposed strategy, the robust FTTC problem for a class of LPV systems with state/input constraints is considered. Overall, the involved key designs consist of three parts. First, an unconstrained FTTC component is constructed by combining tracking error feedback, reference input regulation, and fault signal compensation. It is used to guarantee the robust tracking stability of closed-loop systems when the constraints are not activated. Second, a tube-based predictive FTTC policy with an embedded optimal preview regulator is designed to achieve the robust constraint satisfaction and transient response improvement. Third, an embedded reference governor is additionally integrated to significantly enlarge the size of the fault-tolerant admissible region. This design further reinforces the feasibility of constrained FTTC optimization. The effectiveness of these results is finally validated by a case study of a single transistor dc/dc Forward converter.

This article proposes a novel unknown input functional observer design approach toward discrete-time interval type-2 Takagi–Sugeno fuzzy system models subject to measurable and unmeasurable premise variables. By constructing a new state vector that contains both the unknown inputs and the system states, functional observers are proposed for the cases with measurable and unmeasurable premise variables to estimate this new state vector for unknown input and/or state estimation. The observer design problem is converted into the solvability issue of a linear matrix equation involving observer gain matrices, and the existence conditions of the observers are explicitly obtained based on matrix rank analysis. Meanwhile, instead of solving the intricate Sylvester equation directly, the solution of the simplified matrix equation is employed to derive the observer gains. Moreover, the effectiveness and the superiority of the presented method are demonstrated via two illustrative examples.

This paper addresses the finite horizon H∞fault estimation problem for 2-D linear discrete time-varying systems with bounded unknown input and measurement noise. The main contribution of this paper is the H∞fault estimator for 2-D systems with a necessary and sufficient existence condition. By introducing a partially equivalent stochastic dynamic system in Krein space, the necessary and sufficient condition for the existence of the H∞fault estimator is derived based on innovation analysis and projection formula in Krein space. Then, the solution of the estimator is achieved by means of a Riccati-like difference equation for 2-D systems. Finally, a thermal process example is given to demonstrate the effectiveness of the proposed method.

Experience replay is a promising approach to improve the learning efficiency of adaptive dynamic programming. A general model-free adaptive dynamic programming (ADP) approach with the experience replay technology is investigated in this paper to solve the optimal control problems in continuous state and action spaces. Both the critic network and action network are modeled with a feedforward neural network with one hidden layer. During the learning process, a number of recently observed data samples are recorded in a database. When updating the parameters of the neural networks, the data in the sample database are repeatedly used to update the weights of the action network and the critic network. Implementation details of the algorithm are given, and simulation experiments are utilized to verify the learning efficiency of the proposed approach.