Stochastic Process Decision Methods for Complex-Cyber-Physical Systems

Willcox, Karen; Allaire, Douglas; Deyst, John; He, Changjiang; Sondecker, G.

doi:10.21236/ada552217

Cited by 15 publications

(7 citation statements)

References 8 publications

(14 reference statements)

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“…Spectral examples of nonentropic graph measure are maximum eigenvalue of adjacency matrix [49], sum of eigenvalues of adjacency matrix (also known as graph energy [6]), and determinant of adjacency matrix (which is the product of eigenvalues). Kolmogorov graph complexity measures such as linear graph complexity [50] are based on size of the minimal code that reconstructs the graph.…”

Section: O M P L E X I T Ymentioning

confidence: 99%

“…In product engineering limited efforts have been focused on developing complexity measures that are helpful in estimating the amount of resources required to complete a design task, and to estimate design difficulty [4]. A recent research project (META) sponsored by DARPA (Defence Advanced Research Project Agency) focused on identifying complexity metrics for engineered systems that correlate with as well as predict project cost, schedule or reliability, which can also be used to compare designs/system concepts alternatives [5][6][7]. However, there is no consensus about how to measure complexity in the context of systems engineering, which is further exacerbated by the fact that the applicability of such complexity measures to predict project success remains fundamentally dubious [8].…”

Section: Introductionmentioning

confidence: 99%

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A general framework for measuring system complexity

Efatmaneshnik

Ryan

2016

Complexity

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In this work, we are motivated by the observation that previous considerations of appropriate complexity measures have not directly addressed the fundamental issue that the complexity of any particular matter or thing has a significant subjective component in which the degree of complexity depends on available frames of reference. Any attempt to remove subjectivity from a suitable measure therefore fails to address a very significant aspect of complexity. Conversely, there has been justifiable apprehension toward purely subjective complexity measures, simply because they are not verifiable if the frame of reference being applied is in itself both complex and subjective. We address this issue by introducing the concept of subjective simplicity—although a justifiable and verifiable value of subjective complexity may be difficult to assign directly, it is possible to identify in a given context what is “simple” and, from that reference, determine subjective complexity as distance from simple. We then propose a generalized complexity measure that is applicable to any domain, and provide some examples of how the framework can be applied to engineered systems. © 2016 Wiley Periodicals, Inc. Complexity 21: 533–546, 2016

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Section: O M P L E X I T Ymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A general framework for measuring system complexity

Efatmaneshnik

Ryan

2016

Complexity

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“…The blocks that Depending on the specific subsystem, different models can be used. The MIT META research group is developing a fundamental theory to quantify inherent uncertainties and risks in complex system design and development processes [136].…”

Section: Model Generalizationsmentioning

confidence: 99%

Statistical Risk Estimation for Communication System Design

Babuscia

Cheung

2013

IEEE Systems Journal

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Spacecraft are complex systems that involve many subsystems and multiple relationships among them. The design of a spacecraft is an evolutionary process that starts from requirements and evolves over time. During this process, changes can affect mass and power at component, subsystem, and system level. Each spacecraft has to respect overall constraints in terms of mass and power. The current practice in system design deals with this problem by allocating margins to individual components and to individual subsystems. However, a statistical characterization of the fluctuations in mass and power of the overall system (i.e. the spacecraft) is missing. This lack can result in a risky spacecraft design that might not fit the mission constraints and requirements, or in a conservative design that might not fully utilize the available resources. This problem is especially challenging at the initial stage of the design, when high levels of uncertainty due to lack of knowledge are unavoidable.This research proposes a statistical approach to quantify the likelihood that the design of a spacecraft would meet the mission constraints in mass and power consumption, focusing on the initial stage of the design. Due to the complexity of the problem and the different expertise required to develop a complete risk model for a spacecraft design, the scope of this research is focused on risk estimation for a specific spacecraft subsystem: the communication subsystem. The current research aims to be a "proof of concept" of a risk-based design approach, which can then be further expanded to the design of other subsystems as well as to the whole spacecraft.The approach presented in this thesis includes a baseline communication system design tool, and a statistical characterization of the design risks through a combination of historical mission data and expert opinion. Different statistical techniques are explored to ensure that the amount of information extracted from data and expert opinion is maximized. Specifically, for statistics based on data, Kernel Density Estimator is selected as the preferred technique to extract probability densities from a database of previous space missions' components. Expert elicitation is generated 3 through a four-part model which quantifies experts' sensitivity to biases, and uses this measurement to compose properly the assessments from different experts. Finally, an optimization framework is developed to compare multiple possible design architectures, and to select the one that minimizes design objectives, like mass and power consumption, while minimizing the risk associated with the same metrics.Examples of missions are applied to validate the model. Results show that the statistical approach recognizes whether the initial estimate of the system is an overestimation or an underestimation, providing a valuable tool to measure the risk of a communication system at the initial state of the design. Specifically, statistics based on historical data and on expert elicitation allow the designer to siz...

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“…Given its complexity (i.e., procedures and interactions among its subsystems) and even if the definition of complexity is not always clear (Efatmaneshnik and Ryan, 2016), to examine a railway system in its entirety poses considerable difficulties. Willcox et al (2011) agreed on defining the complexity as the ability of the system to produce unexpected states, drifting from the design phase. Consequently, this paper focuses solely upon railway maintenance, given that railway maintenance is less researched compared with railway operations.…”

Section: Introductionmentioning

confidence: 99%

Wheel maintenance in rolling stock: safety challenges in the defect detection process

Chatzimichailidou

Martinetti

Majumdar³

et al. 2018

IJSSE

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The proper and timely maintenance of railway rolling stock is essential for the safety of railway operations. Inaccurate inspection can lead to inadequate repair of defects and to great safety challenges in respect to the entire railway system. The detection and repair of any defect, such as cracks, in the wheels prior to a failure can significantly reduce train derailments and improved operational performance. This paper examines the wheel maintenance process in maintenance depots in the Netherlands on the basis of literature review, observations and interviews. First, it highlights various detection methods, including the risks of the incorrect detection of a flawed wheel profile. This paper introduces a flowchart as a concise illustration of the maintenance process; that is, both the detection and treatment of defects. With this in hand, the authors, as well as anyone involved in maintenance, are able to identify the points where the process is vulnerable and may be prone to incidents/accidents. Based on this procedure, improvements to the current wheel maintenance process can be proposed. The method that the flowchart is based on is also presented herein, along with the findings obtained throughout its steps.

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Stochastic Process Decision Methods for Complex-Cyber-Physical Systems

Cited by 15 publications

References 8 publications

A general framework for measuring system complexity

A general framework for measuring system complexity

Statistical Risk Estimation for Communication System Design

Wheel maintenance in rolling stock: safety challenges in the defect detection process

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