Deep learning has been widely applied in the field of rotating machinery fault diagnosis. Existing deep learning-based fault diagnosis methods commonly utilize deep neural networks to extract features from raw data and employ fully connected layers for classification of the extracted features. However, in existing methods, fault type-related information is coupled with irrelevant information (e.g., load variations, fault sizes, and system intrinsic properties) at the fully connected layer. The lack of decoupling may result in fault type-related information being overwhelmed by irrelevant factors, thereby hindering accurate fault type determination. We propose that there exists an intrinsic feature in the data, where information is interrelated and closely tied to the fault type. This feature remains highly stable under changes in irrelevant factors, such as load and fault size, and provides an accurate representation of the fault type. By extracting and analyzing fault-type intrinsic features, the interference caused by irrelevant factors on fault type determination can be effectively eliminated. To address this, this article proposes a novel decoupling generative adversarial network for decoupling the intrinsic feature of fault type. The proposed method effectively extracts the intrinsic features of fault types that are unaffected by irrelevant factors, significantly improving fault diagnosis accuracy and robustness, and, to some extent, endowing the fault-type intrinsic features with interpretability and physical rationality.

High-speed train (HST) positioning plays a vital role in ensuring train safety, improving transportation efficiency and efficient dispatching. As the railway system has adopted and integrated 5G New Radio (NR) technology, higher requirements are placed on HST positioning accuracy. This paper presents a novel extended Kalman filter-based fusion positioning method which incorporates nonlinear high-order variable information from the train’s nonlinear positioning model. In this method, to capture and exploit the high-order variable information in nonlinear positioning models, an auxiliary model that focuses on high-order variables is introduced. The predicted values of the high-order variables obtained from the auxiliary model can be regarded as additional measurements of these variables. This approach thereby circumvents the identification of high-order variables under the condition of under-measurement, which is a common issue in existing methods. Meanwhile, to ensure the accuracy of the introduced auxiliary model, a real-time updating method for the statistical characteristics of the auxiliary model error is proposed. To intuitively compare the performance of the high-order extended Kalman filter under different expansion orders, this paper presents a novel performance index. The simulation results of train positioning under different motion modes and the rotor position of permanent magnet synchronous motors (PMSMs), demonstrate that the second-order extended filter designed in this paper achieves better estimation performance than existing second-order extended filters when only utilizing the second-order variables in the nonlinear model. Moreover, as the information of high-order variables is progressively incorporated, the proposed high-order extended filter exhibits superior performance.

The key equipment in a cyber-physical system (CPS) can be changed or replaced by untrusted equipment, which causes the system to make incorrect decisions, which threatens people's lives and property security. Inspired by the identification of human genes, this research establishes a data fingerprint identification method for equipment. This method provides a security guarantee for the operation of a CPS. Thus, this research makes three contributions. First, it is the first attempt to represent device data information with a mathematical representation model based on multiorder terms. Second, we originally attempt to establish a multigranularity feature extraction method. Then, for the first time, we try to propose different scale matching methods for different granularity features. In addition, an F-404 aircraft example is used to verify the new method. The experimental results show that stealthy false data attacks can bypass the${\chi ^{2}}$detector and destroy the operation of key equipment. However, the method proposed in this article can detect stealthy false data attacks and alert managers so that they can deal with the attacks in time; hence, the method proposed in this article is better than the${\chi ^{2}}$detector. Similarly, we contrast the proposed method with recent competitor schemes and provide tangible evidence of the effectiveness of the proposed solution.

Considering disturbances, noise, and sensor faults, this article investigates interval estimation for discrete-time Takagi–Sugeno fuzzy systems. To obtain precise estimation results and attenuate disturbances and noise in the system simultaneously, we integrate robust observers based on the$ H_{\infty }$technique and reachability analysis. Two novel observer gain computation methods are proposed for different purposes. The time-invariant method relaxes the original design conditions by transforming the parameterized linear matrix inequality (LMI) into a series of LMIs to increase computational speed, while the time-varying method employs the parameterized LMI directly and conducts calculation online. Furthermore, reachable set representations for error dynamics are formulated by making use of both time-invariant observer gain and time-varying observer gain. An inverted pendulum system simulation and a comparative numerical simulation are studied to illustrate the effectiveness and superiority of the developed methods.

The federated learning (FL) method based on model aggregation can balance data and protect data privacy, but the existing method is difficult to achieve the same effectiveness as the centralized learning method under data sharing. In addition, it is difficult for the existing federated model to realize the real-time update of the clients' network parameters, because it inhibits the optimal performance of the client. Therefore, this article proposes an asynchronous update paradigm of FL with real-time identification of the client's network parameters to tackle the shortcomings. First, we adopt the linear fusion method based on sequential filtering and fuse the parameters of federated center asynchronously considering communication delay, which can approach the diagnostic accuracy based on the centralized learning. Second, we establish the real-time identification method for the clients based on linear filtering with the new labeled samples obtained at nonequal intervals, which expects the client to acquire better performance. Finally, we test the fault classification ability of the proposed method based on the actual collected fault dataset and the test platform of bearing fault dataset.

The belief rule-base (BRB) model is a new intelligent expert system with the characteristics of both expert system and data-driven model. In BRB there are many if-then rules which use belief degrees to express various types of uncertain information, including fuzziness, randomness, and ignorance. As a semi-quantitative modeling tool for complex systems, BRB has the superiorities of dealing both numerical quantitative data and linguistic qualitative knowledge that are derived from heterogeneous sources. Moreover, it is also a white box approach which can provide direct access and transparency to decision makers and stakeholders. Currently, BRB has been widely applied in many fields, such as decision making, reliability evaluation, network security situation awareness, fault diagnosis, and so on. To fully demonstrate the progress of BRB, the original BRB, and some evolution forms are introduced in this article.

The lack of traffic data is a bottleneck restricting the development of Intelligent Transportation Systems (ITS). Most existing traffic data completion methods aim at low‐dimensional data, which cannot cope with high‐dimensional video data. Therefore, this paper proposes a traffic data complete generation adversarial network (TDC‐GAN) model to solve the problem of missing frames in traffic video. Based on the Feature Pyramid Network (FPN), we designed a multiscale semantic information extraction model, which employs a convolution mechanism to mine informative features from high‐dimensional data. Moreover, by constructing a discriminator model with global and local branch networks, the temporal and spatial information are captured to ensure the time‐space consistency of consecutive frames. Finally, the TDC‐GAN model performs single‐frame and multiframe completion experiments on the Caltech pedestrian dataset and KITTI dataset. The results show that the proposed model can complete the corresponding missing frames in the video sequences and achieve a good performance in quantitative comparative analysis.

In this paper, adaptive quantized state estimation fusion is deeply studied. To approach the model mismatching problem induced by random quantization, some quantized Kalman filters have been presented in the previous work, such as the quantized Kalman filter with strong tracking filtering (QKF-STF), the variational Bayesian adaptive quantized Kalman filter (VB-AQKF), and a centralized fusion frame-based complex quantized filter called variational Bayesian adaptive QKF-STF (VB-AQKF-STF). Based on the previous work for the single sensor system, a distributed complex quantized filter is designed in this paper. A novel quantized Kalman filter based on multiple-method fusion scheme (QKF-MMF) is proposed. Similar to the VB-AQKF-STF, the QKF-MMF can also realize joint estimation on the state and the quantization error covariance under the distributed fusion frame. Furthermore, it extends the single sensor results to multisensor tracking systems by using centralized and distributed fusion frames. Two multisensor quantized fusion estimators are proposed for a parallel structure with main-secondary processors in the fusion center. The weighted fusion and embedded integration ways are deeply applied to design the multisensor quantized fusion methods. The proposed work can perfect the quantized estimation algorithms and provide different choices for practical engineering applications.

Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise (1*1) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10~1/100 network parameters and computational cost while achieving comparable performance.

Permanent magnet synchronous motor with its high torque density, high efficiency and high reliability, has become the mainstream of the drive system for electric vehicles, elevators, etc. The existing method of fault diagnosis based on the motor model has not considered the fractional characteristics of motor. it is difficult to effectively diagnose minor faults of the current. With the permanent magnet synchronous motor as object, based on fractional mathematical model and unscented kalman filter, establish the minor fault diagnosis method for current. Experiments show that the integer order model is difficult to detect the minor fault, while the fractional model can achieve fault diagnosis.

In this letter, we propose a novel statistical active contours method for using an arbitrary number of level set functions to segment an image into regions of the corresponding amount. First, a new updating of level set functions for every region is derived from the probabilistic models of image data. Second, a novel geometric prior that deduced from the level-set-based curve evolution is introduced to obtain the probabilistic label. Therefore, updating of the level set functions and estimation of the distribution parameter are run alternately in a fast manner. Finally, in order to further enhance the efficiency, we initialize the level set function by the statistical approach to draw near object boundary. We experimentally evaluate our proposed approach on complicated real-world images and demonstrate its good performance in practice.

The special characteristics of slowly moving infrared targets, such as containing only a few pixels, shapeless edge, low signal-to-clutter ratio, and low speed, make their detection rather difficult, especially when immersed in complex backgrounds. To cope with this problem, we propose an effective infrared target detection algorithm based on temporal target detection and association strategy. First, a temporal target detection model is developed to segment the interested targets. This model contains mainly three stages, i.e., temporal filtering, temporal target fusion, and cross-product filtering. Then a graph matching model is presented to associate the targets obtained at different times. The association relies on the motion characteristics and appearance of targets, and the association operation is performed many times to form continuous trajectories which can be used to help disambiguate targets from false alarms caused by random noise or clutter. Experimental results show that the proposed method can detect slowly moving infrared targets in complex backgrounds accurately and robustly, and has superior detection performance in comparison with several recent methods.

Performance of the Kalman filter (KF) is degraded when dealing with nonlinear dynamic systems. For a kind of nonlinear biomechatronics system, a fifth-degree ensemble iterated cubature square-root information filter (EsFICIF), which can effectively improve estimation performance, is proposed by combing many estimation schemes. Moreover, the associated multisensor fusion is deeply studied based on this proposed nonlinear filter in this paper. That is, four classic nonlinear fusion methods, which include augmented measurements fusion, weighted measurements fusion, sequential filtering fusion, and distributed filtering fusion, are compared on estimation performance. The motivation of this paper is to extend the work on estimation performance comparison of nonlinear fusion methods based on the conventional extended KF and to validate some basic conclusions existed in the traditional linear data fusion theory based on the proposed EsFICIF. The estimation accuracies of the four nonlinear fusion methods are compared and the exchanging property of measurements update order is also discussed. It is observed that, when the measurement properties are identical, the estimation accuracies of augmented measurements fusion, weighted measurements fusion, and distributed feedback fusion are equivalent, while the sequential filtering fusion does not hold. Furthermore, the exchanging property of the measurements update order of the sequential filtering fusion can no longer be guaranteed. These results further show some basic conclusions existed in linear fusion theory are no longer valid for nonlinear systems and the conclusions based on the EKF are still available for more complex nonlinear filters. Finally, numerical examples are provided to validate the results given in this paper.

Square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter. In this paper, we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change. First, we give the SCKF that deals with the one step correlation between process and measurement noises, SCKF-CN in short. Second, we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error covariance with a fading factor, which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change. Accordingly, the tracking performance of SCKF is greatly improved. A universal nonlinear estimator is proposed, which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises, but also achieve an excellent strong tracking performance towards the abrupt change of target state. Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.