With the wide application of cyber-physical systems, stealthy attacks on remote state estimation have attracted increasing research attention. Recently, various stealthy innovation-based linear attack models were proposed, in which the relaxed stealthiness was measured by the Kullback--Leibler divergence. This paper studies existing innovation-based linear attack strategies with relaxed stealthiness and concludes that all of them provided merely suboptimal solutions. The main reason is some oversight in solving the involved optimization problems: some covariance constraints were not perfectly handled. This paper provides the corresponding optimal solutions for those stealthy attacks. Both one-step and holistic optimizations of stealthy attacks are studied, and the worst-case attacks with and without zero-mean constraints are derived analytically, without the necessity to formulate semidefinite programming problems.

This paper presents an encryption-based countermeasure against stealthy attacks on remote state estimation. Smart sensors transmit data to a remote estimator through a wireless communication network, in which data packets can be intercepted and compromised by attackers. The remote end is equipped with a false data detector monitoring the system. To avoid being detected, the attack should follow the stealthiness constraint. A linear encryption scheme is proposed to reduce the influence of potential stealthy attacks. For arbitrary linear encryption, the worst-case linear attack which yields the largest estimation error is derived. Accordingly, the optimal linear encryption which minimizes the worst-case estimation error is designed based on the Stackelberg game analysis. The above optimal strategies are considered in the scenarios of complete and partial measurement information for the attacker. Moreover, the generalization to nonlinear encryption strategies is also discussed. Comparisons of attack and encryption strategies through simulated examples are provided to illustrate the theoretical results.

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.

Blast furnaces are the most crucial equipment in ironmaking processes. Stable operation of the blast furnace is a prerequisite for personnel safety and production efficiency. Therefore, early detection of abnormalities in blast furnaces is an important task for ironmaking processes. However, owing to the large fluctuations in the quality of raw materials, dynamic operating conditions, as well as the impact of the hot blast stoves switches, the measurements of blast furnace show severe nonstationary characteristics. All these factors make monitoring the blast furnace a challenging task. In this article, a nonstationary process monitoring method called consistent trend feature analysis (CTFA) is proposed, which can extract the trend-related features and discard perturbations in process data. The directions and amplitudes of the extracted trends are used for abnormality detection, and a local-learning-based method is proposed for determining a time-varying control limit. The detection performance of the proposed method is analyzed, with a sufficient condition and a necessary condition for the detectability given. The effectiveness of the proposed method is validated by the practical data collected from a large-scale blast furnace located in Liuzhou, China.

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.