Semi-infinite programming for global guarantees of robust fault detection and isolation in safety-critical systems

Hale, William T.; Wilhelm, Matthew; Palmer, Kyle A.; Stuber, Matthew D.; Bollas, George M.

doi:10.1016/j.compchemeng.2019.04.007

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…Complex engineering uncertainty analyses where one samples from an output metric's

X

distribution of unknown parametric form have become more prevalent not only in engineering safety analysis, but in engineering in general. That output distribution being of unknown parametric form requires a nonparametric statistical procedure for smallest sample size recommendations such that the sample obtained contains a specified proportion of the output metrics' uncertainty distribution with a certain degree of confidence consistent with safety regulation requirements in engineering see, for example, Reference 11‐13. The two‐quantile Wilks method involving two quantiles

b

and

c

proposed herein is such a nonparametric statistical procedure.…”

Section: Discussionmentioning

confidence: 99%

“…Though assumptions are sometimes made on the nature of the distribution of such an output metric see, for example, References 7‐9, it is quite important to note that deviations or discrepancies in assigning or specifying erroneous densities portend significant impacts 10 . The solemn impacts are more pronounced especially in nuclear and chemical engineering where actual safety limits and regulatory acceptance criteria must be aligned see, for example, References 11 and 12. For example, estimating the “the right” tolerance is crucial in meeting the licensing requirements set forth by the Nuclear Regulatory Commission (NRC) in their specification of the limits of safety criteria 13 .…”

Section: Introductionmentioning

confidence: 99%

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An enhanced two‐quantile Wilks methodology for engineering uncertainty analysis

Dorp

Shittu

Rabiti

2022

Appl Stoch Models Bus & Ind

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Complex computational engineering uncertainty analyses have become more prevalent. When input parameters of such engineering models are uncertain, the output metric's uncertainty distribution is of an unknown parametric form. Since Wilks' method, named after the seminal paper by SS Wilks in 1941 entitled “Determination of sample sizes for setting tolerance limits”, is a nonparametric statistical procedure, it has received renewed interest, in particular in nuclear and chemical safety engineering. Unfortunately, the prevailing Wilks' method applied relies on arbitrary specification of order statistics' ranks with undue influence on the sample size recommendations that follow. Herein, a novel modification of Wilks' method involving two quantiles is proposed resolving that arbitrary rank selection. Together with a confidence level to be exceeded, these quantiles uniquely determine the parameters of an order statistics' beta distribution which drive the selection of symmetric tolerance limits. The modified procedure is demonstrated in two illustrative engineering uncertainty analysis examples drawn from the nuclear and chemical engineering domains.

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“…Complex engineering uncertainty analyses where one samples from an output metric's

X

b

and

c

proposed herein is such a nonparametric statistical procedure.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An enhanced two‐quantile Wilks methodology for engineering uncertainty analysis

Dorp

Shittu

Rabiti

2022

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

show abstract

“…The uniqueness assumption is required for the elimination of the coupling equality constraints from the original problem formulation (eq 2). This assumption and approach have been commonly made in practice for addressing design under uncertainty problems (e.g., Stuber and Barton, 13 Kwak and Haug, 41 Halemane and Grossmann, 42 Ostrovsky et al, 43 Dimitriadis and Pistikopoulos, 44 Hale et al, 45 among others) and is not presented as a new approach here. However, in case of nonunique parametric solutions (e.g., multiple steady states), without special consideration this approach would effectively restrict the feasible set.…”

Section: ■ Optimization Of Hybrid Modelsmentioning

confidence: 99%

Semi-Infinite Optimization with Hybrid Models

Wang

Wilhelm

Stuber

2022

Ind. Eng. Chem. Res.

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The robust design of performance/safety-critical process systems, from a model-based perspective, remains an existing challenge. Hybrid first-principles data-driven models offer the potential to dramatically improve model prediction accuracy, stepping closer to the digital twin concept. Within this context, worst-case engineering design feasibility and reliability problems give rise to a class of semi-infinite program (SIP) formulations with hybrid models as coupling equality constraints. Reduced-space deterministic global optimization methods are exploited to solve this class of SIPs to ϵ-global optimality in finitely many iterations. This approach is demonstrated on two challenging case studies: a nitrification reactor for a wastewater treatment system to address worst-case feasibility verification of dynamical systems and a three-phase separation system plagued by numerical domain violations to demonstrate how they can be overcome using a nonsmooth SIP formulation with hybrid models and a validity constraint incorporated.

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Distributed and explainable GHSOM for anomaly detection in sensor networks

Mignone,

Corizzo,

Ceci

2024

Mach Learn

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The identification of anomalous activities is a challenging and crucially important task in sensor networks. This task is becoming increasingly complex with the increasing volume of data generated in real-world domains, and greatly benefits from the use of predictive models to identify anomalies in real time. A key use case for this task is the identification of misbehavior that may be caused by involuntary faults or deliberate actions. However, currently adopted anomaly detection methods are often affected by limitations such as the inability to analyze large-scale data, a reduced effectiveness when data presents multiple densities, a strong dependence on user-defined threshold configurations, and a lack of explainability in the extracted predictions. In this paper, we propose a distributed deep learning method that extends growing hierarchical self-organizing maps, originally designed for clustering tasks, to address anomaly detection tasks. The SOM-based modeling capabilities of the method enable the analysis of data with multiple densities, by exploiting multiple SOMs organized as a hierarchy. Our map-reduce implementation under Apache Spark allows the method to process and analyze large-scale sensor network data. An automatic threshold-tuning strategy reduces user efforts and increases the robustness of the method with respect to noisy instances. Moreover, an explainability component resorting to instance-based feature ranking emphasizes the most salient features influencing the decisions of the anomaly detection model, supporting users in their understanding of raised alerts. Experiments are conducted on five real-world sensor network datasets, including wind and photovoltaic energy production, vehicular traffic, and pedestrian flows. Our results show that the proposed method outperforms state-of-the-art anomaly detection competitors. Furthermore, a scalability analysis reveals that the method is able to scale linearly as the data volume presented increases, leveraging multiple worker nodes in a distributed computing setting. Qualitative analyses on the level of anomalous pollen in the air further emphasize the effectiveness of our proposed method, and its potential in determining the level of danger in raised alerts.

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Semi-infinite programming for global guarantees of robust fault detection and isolation in safety-critical systems

Cited by 9 publications

References 43 publications

An enhanced two‐quantile Wilks methodology for engineering uncertainty analysis

An enhanced two‐quantile Wilks methodology for engineering uncertainty analysis

Semi-Infinite Optimization with Hybrid Models

Distributed and explainable GHSOM for anomaly detection in sensor networks

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