Detection and classification are two significant tasks for industrial fault diagnosis. However, conventional methods typically treat these tasks as separate and independent problems, and necessitate a retraining process when new fault samples or classes are collected. Therefore, an incremental support vector data description scheme using Gaussian kernel function is pro-posed for industrial process fault diagnosis in a unified frame-work. In this framework, the decision boundary is updated incrementally only based on the specific original support vectors and newly collected samples. An adaptive threshold and a restructured radius are proposed to promote accuracy in the fault detection. In the classification procedure, the hyperspheres for all known classes are constructed by decision tree. The new sample that does not belong to any known class is identified as an unknown class. Without a time-consuming retraining process, the proposed diagnosis method with the incremental learning capability can synchronously achieve the fault detection and classification task. Experimental results demonstrate the effectiveness and superiority in terms of diagnosis performance.Note to Practitioners—In practical industrial processes, new fault samples are collected and new fault classes emerge continually. Under such scenarios, fault diagnosis with incremental learning capability is becoming increasingly important. This work proposes a unified incremental framework for fault diagnosis based on support vector data description, which is able to achieve fault detection and classification synchronously. For fault detection, an adaptive threshold and a restructured radius are developed. For fault classification, hypersphere-shaped boundaries of all fault classes are given via the decision tree-based strategy. The proposed method is updated to include new fault samples and fault classes without dimensionality reduction or any distributional assumptions.

Federated learning (FL) and zero-shot learning have been becoming increasingly popular due to the data-privacy protection and the diagnosis of unseen faults in the industrial fault diagnosis. However, most existing diagnosis methods have the consistency assumption of distributions across different clients under multisource domain scenarios and cannot effectively diagnose both seen and unseen faults. Therefore, to diagnose both seen and unseen faults without data sharing and with distribution discrepancies across different clients, a federated generalized zero-sample fault diagnosis (GZSFD) paradigm is proposed in this article. In the client side, a stacked autoencoder (AE)-based feature extractor is introduced in each client for low-level features. In the cloud server, a feature-level distribution alignment scheme is developed to alleviate discrepancies for more discriminative high-level features. Moreover, a bidirectional AE (BAE) with reconstruction and cross-reconstruction streams is designed to enhance the feature-semantic consistency. Finally, a gating model based on BAE is proposed to identify online samples and mitigates the misclassification of unseen samples. Results on two practical industrial cases show that the proposed method achieves the improvement in federated GZSFD and effectively handles distribution discrepancies across different clients.

Health state prediction and fault time prediction are two key tasks in the fault prediction field. However, existing fault prediction techniques perform these tasks hierarchically and separately without considering the time-varying dynamics of the system operation process, which reduces the prediction efficiency and accuracy. Therefore, a Gaussian process regression prediction method based on the update strategy is proposed for the dual tasks of aeroengines. In this method, for new samples collected continuously, the predictive distributions are deduced and the model parameters are updated. Specifically, through variable selection and multivariable fusion technology, the most beneficial variables corresponding to the health state are used to construct a shared health index for health state and fault time prediction. The proposed health index can better characterize the health state. Then, by the update strategies including single-point and multipoint update strategies, a unified Gaussian process regression framework with newly collected samples information is obtained. Thereby, the health index and fault time prediction are realized synchronously. Experimental results on the commercial modular aero-propulsion system simulation dataset demonstrate that the proposed method outperforms state-of-the-art ones.

The scarcity of fault samples is a common scenario in the field of fault diagnosis. In the context of mechanical fault diagnosis, the emergence of new working conditions and fault modes renders the availability of samples of target (unseen) faults for model training unfeasible, thus limiting the performance of data-driven methods. Consequently, zero-sample learning and diagnosis of mechanical faults is a challenging task. In this regard, this article proposes a multiattribute learning model, inspired by the zero-shot learning paradigm, for zero-sample mechanical fault diagnosis. The key lies in the shared multiclass attribute classifiers. During the attribute learning process, a convolutional neural network is developed to construct multiclass attribute classifiers, which serve as a mapping between visual features and semantic features. These classifiers are transferred from readily available faults to enhance the capability of diagnosing unseen faults. By minimizing the difference among the fault attributes, the diagnosis of unseen faults is achieved, which includes fault location, size, working load, etc. Experiments on two real datasets verify the efficacy and the superiority of the proposed method.

The notable vehicles (NV) predictive cruise cloud control (PCCC) system has great potential in improving the performances of driving safety and energy saving by virtue of advantages on the decent calculation and optimal control capability of cloud control system (CCS). The traditional PCC systems lack the interoperable integration control of cyber layer and physical plane, limited by the range of perception and on-board computing ability, so that the information acquisition and processing are quite restrained, which impede the improvement of control performances to a certain extent. In this study, we propose a CCS layered architecture based on cyber-physical systems (CPS), which effectively addresses the problems of real-time multi-sources heterogeneous information utilization by PCC systems and insufficient computational planning capability of solver of NV. Then, based on the digital information in the cloud control basic platform, we propose an intensified PCCC framework in the hierarchical architecture application platform. In this framework, we introduce a modified deep-learning traffic prediction model to predict the traffic state after pre-factorization processing and the optimization algorithm solver to optimize the proposed scheme under the restrictive constraints via using the prediction information and digital information. We fully evaluate the effectiveness of the architecture under various traffic scenarios through simulation. The results show that the proposed layered architecture has implementation feasibility and great potential in the application of vehicle-cloud layered control.

This study investigates the sample value imbalance problem of process monitoring. A fault detection approach based on variable selection and support vector data description (SVDD) is developed for efficient process monitoring. First, Kullback–Leibler divergence serves as the variable selection algorithm, which highlights the most beneficial information about the concerned faults. The attained variables are segmented by block division to avoid faults information being covered in single space monitoring, so that the relevant variables and the most beneficial information are concentrated in the same block. Then, Kernel principal component analysis is applied in each block to address the challenge that variables may still be high-dimensional and nonlinear. After that, the monitoring result is given based on the proposed SVDD with a restructured radius index, which is more sensitive to the fault. As demonstrated from experimental results on the Tennessee Eastman process, this method is effective and outperforms counterparts with higher mean fault detection rate. Note to Practitioners—Recently, multivariate statistical process monitoring (MSPM) has attracted much attention. In general, MSPM incorporates all variables for the large-scale process. However, only a small number of variables are fault-dependent. Namely, the sample value imbalance problem is encountered in application. In this scenario, the monitoring performance degrades and the online computational complexity increases. To this end, a SVDD-based fault detection method, which considers the fault-related variables, is proposed for process monitoring. The proposed method is verified by the Tennessee Eastman process and it is more sensitive to the concerned fault.

In this paper, we developed a systematic frequency domain approach to analyze adaptive tracking algorithms for fast time-varying channels. The analysis is performed with the help of two new concepts, a tracking filter and a tracking error filter, which are used to calculate the mean square identification error (MSIE). First, we analyze existing algorithms, the least mean squares (LMS) algorithm, the exponential windowed recursive least squares (EW-RLS) algorithm and the rectangular windowed recursive least squares (RW-RLS) algorithm. The equivalence of the three algorithms is demonstrated by employing the frequency domain method. A unified expression for the MSIE of all three algorithms is derived. Secondly, we use the frequency domain analysis method to develop an optimal windowed recursive least squares (OW-RLS) algorithm. We derive the expression for the MSIE of an arbitrary windowed RLS algorithm and optimize the window shape to minimize the MSIE. Compared with an exponential window having an optimized forgetting factor, an optimal window results in a significant improvement in the h MSIE. Thirdly, we propose two types of robust windows, the average robust window and the minimax robust window. The RLS algorithms designed with these windows have near-optimal performance, but do not require detailed statistics of the channel.>

One of the main problems in the proposed Northern American digital cellular system (IS-54) is the poor performance of the receiver for fast fading channels. The authors propose to use a novel joint data and channel estimation technique to improve performance. The basic idea of this method was described by N. Seshadri (1990) in the context of blind equalization. Simulation results indicate that this approach solves the problems of decision delay and divergence caused by error decision feedback in conventional maximum likelihood sequence estimation (MLSE). The specified IS-54 requirement of 19 dB at a BER of 3% and a speed of 100 km/h can be met with a margin of 8 dB. This result is 4.5 dB better than the conventional maximum likelihood receiver.>