Process Monitoring for Multimodal Processes With Mode-Reachability Constraints

Afzal, Muhammad Shahzad; Tan, Wen; Chen, Tongwen

doi:10.1109/tie.2017.2677351

Cited by 32 publications

(15 citation statements)

References 31 publications

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“…14 n > D(T). (10) Under this circumstance, the transitions between operating modes are subject to the attack strategy and no longer satisfy the regular transition probability a ij given in Equation (8). Figure 1 illustrates the system behavior under attack.…”

Section: Process Under the Attack Subject To Outliersmentioning

confidence: 99%

“…Since the transition probability of HMM is able to describe the transition behavior between various operating modes, the system dynamics can be modeled as a HMM. 10,11 For example, a HMM-based process monitoring approach is proposed to deal with the multimodality of process data and capture the mode switching restrictions. 10 Fang et al 11 model the switching between operating modes by using the conditional random field, which is an extension to HMM.…”

Section: Introductionmentioning

confidence: 99%

“…10,11 For example, a HMM-based process monitoring approach is proposed to deal with the multimodality of process data and capture the mode switching restrictions. 10 Fang et al 11 model the switching between operating modes by using the conditional random field, which is an extension to HMM. However, when the Stuxnet cuts off control loops and injects false data to manipulate operating systems, the transition probabilities of the hidden states will be different from the regular probabilities.…”

Section: Introductionmentioning

confidence: 99%

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Robust algorithm for attack detection based on time‐varying hidden Markov model subject to outliers

Dai

Huang

2020

Adaptive Control & Signal

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The problem of robust attack detection and prediction for networked control systems in the presence of outliers is discussed in this article. The conventional hidden Markov model (HMM) is trained to learn the system behavior (ie, transitions between different operating modes) in the nominal process. The HMM with time-varying transition probabilities is used to track the attack behavior in which the adversary triggers more hazard modes to hasten fatigue of control devices by injecting attack signals with random magnitude and frequency. For different operating modes, the observations are assumed to follow different multivariate Student's t distributions instead of Gaussian distributions and thus address the robust estimation problem. The expectation maximization algorithm is used to estimate parameters. Finally, simulations are conducted to verify the effectiveness of the proposed method.

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Section: Process Under the Attack Subject To Outliersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust algorithm for attack detection based on time‐varying hidden Markov model subject to outliers

Dai

Huang

2020

Adaptive Control & Signal

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Section: Introductionmentioning

confidence: 99%

“…Labeled multi-class data are ubiquitous in a variety of areas and disciplines, such as process monitoring [1], multimedia studies [2,3], and machine learning [4]. For example, process data are labeled with the operating modes of chemical plants [1], and text documents are tagged with labels to indicate their topics [5]. Due to the massive amount of available uncertainty data (realizations of uncertain parameters) and dramatic progress in big data analytics [6], data-driven optimization emerges as a promising paradigm for decision making under uncertainty [7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Data-driven stochastic robust optimization: General computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era

Ning

You

2018

Computers & Chemical Engineering

113

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A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels.Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for uncertainty modeling. A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure. The outer optimization problem follows a two-stage stochastic programming approach to optimize the expected objective across different data classes; adaptive robust optimization is nested as the inner problem to ensure the robustness of the solution while maintaining computational tractability. A decomposition-based algorithm is further developed to solve the resulting multi-level optimization problem efficiently. Case studies on process network design and planning are presented to demonstrate the applicability of the proposed framework and algorithm.

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