Despite growing interest in data-driven analysis and control of linear systems, descriptor systems (or singular systems)—which are essential for modeling complex engineered systems with algebraic constraints like power and water networks— have received comparatively little attention. This paper develops a comprehensive data-driven framework for analyzing and controlling discrete-time descriptor systems without relying on explicit state-space models. We address fundamental challenges posed by non-causality through the construction of forward and backward data matrices, establishing data-based sufficient conditions for controllability and observability in terms of input-output data, where both R-controllability and C-controllability (R-observability and C-observability) have been considered. Building on them, we then extend Willems’ fundamental lemma to incompletely controllable descriptor systems. These methodological advances Data-Enabled Predictive Control (DeePC) for descriptor systems to achieve output tracking and to maintain performance under incomplete controllability conditions, as demonstrated in two case studies: i) Frequency regulation in an IEEE 9-bus power system with 3 generators, where DeePC maintained the frequency stability of the power system despite deliberate violations of R-controllability, and ii) Pressure head control in an EPANET water network with 3 tanks, 2 reservoirs, and 117 pipes, where output tracking was successfully enforced under algebraic constraints.

Accurately identifying the root causes of alarm floods is essential for safe and efficient operations of industrial processes. While similarity analysis methods have been developed for analyzing and clustering alarm floods, they are insufficient to pinpoint root causes. Thus, recent studies have attempted to train classification models to identify alarm flood root causes usually based on substantial amounts of labeled training data, which is impractical and costly in real-world scenarios. Accordingly, this article proposes a semisupervised method named alarm dual-graph fusion-based manifold regularization for root-cause identification (RCI) of industrial alarm floods, which can achieve improved performance by utilizing both limited labeled alarm flood sequences and substantial unlabeled sequences. The contributions are as follows: first, a local sequence alignment-based graph construction method is proposed to construct an undirected graph for historical alarm flood sequences; second, a modified manifold regularization method based on dual-graph Laplacian fusion is developed for semisupervised alarm flood RCI modeling; third, an online RCI strategy is devised for recognition of root causes of incoming alarm flood sequences. The effectiveness of the proposed method is demonstrated by a case study with alarm data generated by the public vinyl acetate monomer process simulation model.

This study explores linear encryption techniques to protect against information-based stealthy attacks on re-mote state estimation. Utilizing smart sensors equipped with local Kalman filters, the system transmits innovations rather than raw measurements via wireless networks. However, this transmission is susceptible to malicious data interception and manipulation by attackers. To safeguard against these stealthy threats, encryption and decryption modules are integrated into the system. This research aims to assess the effectiveness of the encryption strategy when faced with information-based stealthy attacks. A key contribution of this paper is the adoption of the most comprehensive attack models, moving away from the conventional reliance on innovation-based linear attack models. Our results demonstrate that the proposed linear encryption approach effectively mitigates stealthy attacks under certain mild conditions. The efficacy of the encryption is further validated through numerical examples, corroborating the theoretical advancements presented in this paper.

Early classification of ongoing alarm floods in industrial monitoring systems is crucial to provide a safe and efficient operation. It can provide online decision support for plant operators to take timely action, without waiting for the end of an alarm flood. In this article, a data-driven approach is proposed to address the early classification problem with unlabeled historical data. To prioritize earlier activated alarms and take advantage of the triggering time information of alarms, a vector representation called exponentially attenuated component (EAC) is used to represent alarm floods. This makes alarm sequences fit for different powerful machine learning algorithms, which can be easily implemented online with acceptable computational complexities. A method based on the time information of unlabeled historical alarm floods is formulated to determine the attenuation coefficient for EAC representation. With the Gaussian mixture model, an efficient semisupervised approach is proposed to provide an early classification of alarm floods using unlabeled historical data. It includes two phases: offline clustering and online classification, where the clustering step is automated in terms of choosing the optimal number of clusters by applying an efficient cluster validity index. The efficiency of the proposed method is validated by the Tennessee Eastman process benchmark and a real industrial dataset.

This paper studies the problem of false-data injection attacks on remote state estimation. In contrast to existing work that presupposed linear attack models, the optimal information-based attack policy that can cause the maximum estimation quality degradation and deceive the interval χ2detector is obtained. The scenarios that attackers have different information sets from the remote estimator are studied in a unified framework. It is shown that, with the given information set, there does not exist another attack policy outperforms the proposed one. The result in this work reduces to the optimal innovation-based linear attack in a special case. The optimality of the information-based strategy is verified by theoretical analysis and numerical examples.

The significant advancement in data-driven fault detection has been made, but incipient faults such as faults 3, 9, and 15 in Tennessee Eastern process (TEP) still remain difficult for the current approaches. In this article, a powerful principal component analysis (PCA)-based ensemble detector (PCAED) is developed for detecting incipient faults. To begin with, multiple PCA-based detectors are designed based on bootstrap sampling in the training dataset. It can generate two matrices according to principal component and residual subspaces. Then, two sensitive detection indices are developed using maximal singular values of one-step sliding windows along the rows of the above two matrices. With this kind of detection index, PCAED can effectively detect incipient faults, specially faults 3, 9, and 15 in TEP, which cannot be detected by an individual PCA detector. Simulations of TEP and a practical coal pulverizing system fully verify the effectiveness of PCAED. Faults can be successfully detected at the incipient stage, which is very helpful to avoid possible economic or human loss.

For some practical industrial systems or components, such as blast furnaces and Li-ion batteries, there are two important factors to model the degradation processes. One is the long-range dependence, which can reflect the non-Markovian nature of the degradation processes. The other factor is the existence of multiple modes, because the operating conditions and external environments inevitably change during the whole lifetime of these systems. In this paper, we first propose a fractional Brownian motion (FBM) based degradation model with long-range dependence and multiple modes, and then consider the prediction of remaining useful life. To identify the multiple modes in the degradation process, we propose a two-step method, including change-points detection and linear segments clustering. In each degradation mode, the degradation rate is assumed to be normally distributed. The means and variances of these distributions can be obtained by the maximum likelihood estimation. To describe the switching between different modes, the continuous-time Markov chain is applied, and its transition rate matrix can be estimated by the historical switching time. An approximation of the first passage time with a predefined threshold can be obtained by a weak convergence theorem and a time-space transformation. A numerical simulation and a practical case of a blast furnace wall are provided to demonstrate the effectiveness of the proposed method.

The binary feature descriptors such as binary robust independent elementary features (BRIEF), oriented rotated binary robust independent elementary features (ORB), and fast retina keypoint (FREAK) usually perform binarisation on the intensity comparisons, thus they lose some useful information. In this study, the authors propose an effective binary image descriptor which is called significant bit‐planes‐based local binary pattern for visual recognition. First, the authors divide an image into several sub regions according to the intensity orders to incorporate the spatial information. Then the authors extract the higher bit planes for all the sub regions and sort the adjacent neighbour bits based on the corresponding intensity orders, which make the descriptor invariant to rotation. In order to further improve the discriminative ability, the authors sample the multi‐scale neighbours and average the adjacent pixels and extract the feature descriptor from the higher bit planes. Since the authors directly perform operation on the significant bit planes without quantisation, the authors decrease the information loss to some extent. The descriptor has demonstrated a better performance over the state‐of‐the‐art binary descriptors as well as scale invariant feature transform on two recognition benchmarks (i.e. Kentucky and ETHZ) and PASCAL 2007 for image classification.