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Modern industrial control systems (ICSs), which consist of sensor nodes, actuators, and buses, contribute significantly to the enhancement of production efficiency. Massive node arrangements, security vulnerabilities, and complex operating status characterize ICSs, which lead to a threat to the industrial processes' stability. In this work, a condition-monitoring method for ICSs based on canonical variate analysis with probabilistic principal component analysis is proposed. This method considers the essential information of the operating data. Firstly, the one-way analysis of variance method is utilized to select the major variables that affect the operating performance. Then, a concurrent monitoring model based on probabilistic principal component analysis is established on both the serially correlated canonical subspace and its residual subspace, which is divided by canonical variate analysis. After that, monitoring statistics and control limits are constructed. Finally, the effectiveness and superiority of the proposed method are validated through comparisons with actual drilling operations. The method has better sensitivity than traditional monitoring methods. The experimental result reveals that the proposed method can effectively monitor the operating performance in a drilling process with its highest accuracy of 92.31% and a minimum monitoring delay of 11 s. The proposed method achieves much better effectiveness through real-world process scenarios due to its distributed structural division and the characteristic canonical analysis conducted in this paper. [ABSTRACT FROM AUTHOR]

In this study, a signal-off photoelectrochemical aptasensing approach was designed for quantitative screening of carcinoembryonic antigen (CEA) by using cobalt sulfide nanoparticle-functionalized cadmium sulfide nanoflowers (Co9S8@CdS) as photoactive matrix and CuS nanoparticles (NPs) as electronic extinguisher, coupling with target-induced hybridization chain reaction (HCR). The Co9S8@CdS nanohybrids, which were synthesized using a two-step thermal process, shorten the electron-migration path by virtue of their matched and close band positions, thus facilitating carrier separation and increasing photoactivity. In the sensing process of CEA, a sandwiched reaction occurred followed by the smooth implementation of the HCR, accompanied by the introduction of a huge amount of CuS NPs on the photoelectrode surface. These captured CuS NPs increased the consumption of electron donors (ascorbic acid, AA) and excitation light energy and also impeded the movement of AA toward the electrode, which synergistically led to an obvious decrease in photocurrent. Under optimum conditions, the as-designed aptasensor exhibited satisfactory analytical performance for the target CEA with a dynamic linear range from 0.02 to 40.0 ng mL−1 and a detection limit of 7.4 pg mL−1. Besides, this sensor possessed high specificity, good stability, and acceptable accuracy. The current results suggest that our proposed strategy might have broad application prospects in the bioanalysis of disease-related markers and early disease diagnosis. [ABSTRACT FROM AUTHOR]