A Generic Fusion Platform of Failure Diagnostics for Resilient Engineering System Design

2015 ◽  
Author(s):  
Amirmahyar Abdolsamadi ◽  
Pingfeng Wang ◽  
Prasanna Tamilselvan
Keyword(s):  
Majority Voting ◽  
Classification Model ◽  
Engineering System ◽  
Complex Engineered Systems ◽  
Attribute Classification ◽  
Engineered Systems ◽  
Fusion Diagnostics ◽  
Engineering System Design ◽  
Multiple Membership ◽  
Fusion Approach

Effective health diagnostics provides benefits such as improved safety, improved reliability, and reduced costs for the operation and maintenance of complex engineered systems. This paper presents a multi-attribute classification fusion approach which leverages the strengths provided by multiple membership classifiers to form a robust classification model for structural health diagnostics. The developed classification fusion approach conducts the health diagnostics with three primary stages: (i) fusion formulation using a k-fold cross validation model; (ii) diagnostics with multiple multi-attribute classifiers as member algorithms; and (iii) classification fusion through a weighted majority voting with dominance system. State-of-the-art classification techniques from three broad categories (i.e., supervised learning, unsupervised learning, and statistical inference) are employed as the member algorithms. The developed classification fusion approach is demonstrated with the 2008 PHM challenge problem. The developed fusion diagnostics approach outperforms any stand-alone member algorithm with better diagnostic accuracy and robustness.

Design of a Robust Classification Fusion Platform for Structural Health Diagnostics

2013 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Pingfeng Wang ◽  
Chao Hu
Keyword(s):  
Majority Voting ◽  
Classification Model ◽  
Robust Classification ◽  
Structural Health ◽  
Study Results ◽  
Attribute Classification ◽  
Engineered Systems ◽  
Multiple Membership ◽  
Fusion Approach

Efficient health diagnostics provides benefits such as improved safety, improved reliability, and reduced costs for the operation and maintenance of engineered systems. This paper presents a multi-attribute classification fusion approach which leverages the strengths provided by multiple membership classifiers to form a robust classification model for structural health diagnostics. Health diagnosis using the developed approach consists of three primary steps: (i) fusion formulation using a k-fold cross validation model; (ii) diagnostics with multiple multi-attribute classifiers as member algorithms; and (iii) classification fusion through a weighted majority voting with dominance system. State-of-the-art classification techniques from three broad categories (i.e., supervised learning, unsupervised learning, and statistical inference) were employed as the member algorithms. The proposed classification fusion approach is demonstrated with a bearing health diagnostics problem. Case study results indicated that the proposed approach outperforms any stand-alone member algorithm with better diagnostic accuracy and robustness.

Resilience Analysis and Allocation for Complex Systems Using Bayesian Network

2015 ◽  
Author(s):  
Nita Yodo ◽  
Pingfeng Wang
Keyword(s):  
Complex Systems ◽  
Bayesian Network ◽  
Industrial Applications ◽  
Engineering System ◽  
Supply Chain System ◽  
Complex Engineered Systems ◽  
Engineered Systems ◽  
Engineering System Design ◽  
Allocation Approach

The concept of resilience has been explored in diverse disciplines. However, there are only a few which focus on how to quantitatively measure engineering resilience and allocate resilience in engineering system design. This paper is dedicated to exploring the gap between quantitative and qualitative assessments of engineering resilience in the domain of designing complex engineered systems, thus optimally allocating resilience into subsystems and components level in industrial applications. A conceptual framework is first proposed for modeling engineering resilience, and then Bayesian Network is employed as a quantitative tool for the assessment and analysis of engineering resilience for complex systems. One industrial-based case study, a supply chain system, is employed to demonstrate the proposed approach. The proposed resilience quantification and allocation approach using Bayesian Networks would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.

Concept Drift and Evolution Detection in Fusion Diagnosis With Evolving Data Streams

2017 ◽  
Author(s):  
Amirmahyar Abdolsamadi ◽  
Pingfeng Wang
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Data Distribution ◽  
Streaming Data ◽  
Majority Voting ◽  
Classification Model ◽  
Engineering System ◽  
Concept Evolution ◽  
Adaptive Fusion

Health diagnosis interprets data streams acquired by smart sensors and makes inferences about health conditions of an engineering system thereby making critical operational decisions. A data stream is a flow of continuous data that face some challenges in data mining. This paper addresses concept drift and concept evolution as two major challenges in the classification of streaming data. Concept drift occurs as a result of data distribution changes. Concept evolution happens when new classes appear in the stream. These changes may cause the degradation of classification results over time. This paper presents an adaptive fusion learning approach to build a robust classification model. The proposed approach consists of three steps: (i) proposed fusion formulation using weighted majority voting (ii) active learning to labels selectively instead of querying for all true labels (iii) distance-based approach to monitoring the movement of data distribution. A diagnosis case study has been used to demonstrate the developed fusion diagnosis methodology.

scholarly journals Forensic Engineering Use Of Fault Tree Analysis

2006 ◽  
Vol 23 (2) ◽  
Author(s):  
Frank H. Johnson ◽  
DeWitt William E.
Keyword(s):  
Fault Tree ◽  
Fault Tree Analysis ◽  
Tree Analysis ◽  
Complex Engineered Systems ◽  
Track Record ◽  
Forensic Engineering ◽  
Fire And Explosion ◽  
Engineered Systems ◽  
Analytical Tools

Analytical Tools, Like Fault Tree Analysis, Have A Proven Track Record In The Aviation And Nuclear Industries. A Positive Tree Is Used To Insure That A Complex Engineered System Operates Correctly. A Negative Tree (Or Fault Tree) Is Used To Investigate Failures Of Complex Engineered Systems. Boeings Use Of Fault Tree Analysis To Investigate The Apollo Launch Pad Fire In 1967 Brought National Attention To The Technique. The 2002 Edition Of Nfpa 921, Guide For Fire And Explosion Investigations, Contains A New Chapter Entitled Failure Analysis And Analytical Tools. That Chapter Addresses Fault Tree Analysis With Respect To Fire And Explosion Investigation. This Paper Will Review The Fundamentals Of Fault Tree Analysis, List Recent Peer Reviewed Papers About The Forensic Engineering Use Of Fault Tree Analysis, Present A Relevant Forensic Engineering Case Study, And Conclude With The Results Of A Recent University Study On The Subject.

scholarly journals A Multilevel Analysis of Neighbourhood, School, Friend and Individual-Level Variation in Primary School Children’s Physical Activity

2019 ◽  
Vol 16 (24) ◽  
pp. 4889
Author(s):  
Ruth Salway ◽  
Lydia Emm-Collison ◽  
Simon J. Sebire ◽  
Janice L. Thompson ◽  
Deborah A. Lawlor ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vigorous Physical Activity ◽  
Classification Model ◽  
Accelerometer Data ◽  
Individual Level ◽  
Friendship Groups ◽  
Multiple Classification ◽  
Multiple Membership ◽  
Children’S Physical Activity ◽  
Different Levels

Physical activity is influenced by individual, inter-personal and environmental factors. In this paper, we explore the variability in children’s moderate-to-vigorous physical activity (MVPA) at different individual, parent, friend, school and neighbourhood levels. Valid accelerometer data were collected for 1077 children aged 9, and 1129 at age 11, and the average minutes of MVPA were derived for weekdays and weekends. We used a multiple-membership, multiple-classification model (MMMC) multilevel model to compare the variation in physical activity outcomes at each of the different levels. There were differences in the proportion of variance attributable to the different levels between genders, for weekdays and weekends, at ages 9 and 11. The largest proportion of variability in MVPA was attributable to individual variation, accounting for half of the total residual variability for boys, and two thirds of the variability for girls. MVPA clustered within friendship groups, with friends influencing peer MVPA. Including covariates at the different levels explained only small amounts (3%–13%) of variability. There is a need to enhance our understanding of individual level influences on children’s physical activity.

scholarly journals Design of Complex Engineered Systems

2010 ◽  
Vol 132 (12) ◽  
Author(s):  
Christina L. Bloebaum ◽  
Anna-Maria R. McGowan
Keyword(s):  
Complex Engineered Systems ◽  
Engineered Systems

Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Daniel Hulse ◽  
Christopher Hoyle ◽  
Kai Goebel ◽  
Irem Y. Tumer
Keyword(s):  
Functional Model ◽  
Scoring Function ◽  
Modeling Languages ◽  
Concept Selection ◽  
Complex Engineered Systems ◽  
Propagation Behavior ◽  
Engineered Systems ◽  
Selection Framework ◽  
And Function ◽  
Probability Information

Complex engineered systems can carry risk of high failure consequences, and as a result, resilience—the ability to avoid or quickly recover from faults—is desirable. Ideally, resilience should be designed-in as early in the design process as possible so that designers can best leverage the ability to explore the design space. Toward this end, previous work has developed functional modeling languages which represent the functions which must be performed by a system and function-based fault modeling frameworks have been developed to predict the resulting fault propagation behavior of a given functional model. However, little has been done to formally optimize or compare designs based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. The work described herein closes this gap by introducing the resilience-informed scenario cost sum (RISCS), a scoring function which integrates with a fault scenario-based simulation, to enable the optimization and evaluation of functional model resilience. The scoring function accomplishes this by quantifying the expected cost of a design's fault response using probability information, and combining this cost with design and operational costs such that it may be parameterized in terms of designer-specified resilient features. The usefulness and limitations of using this approach in a general optimization and concept selection framework are discussed in general, and demonstrated on a monopropellant system design problem. Using RISCS as an objective for optimization, the algorithm selects the set of resilient features which provides the optimal trade-off between design cost and risk. For concept selection, RISCS is used to judge whether resilient concept variants justify their design costs and make direct comparisons between different model structures.

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