scholarly journals Automated Generation of Fault Scenarios to Assess Potential Human Errors and Functional Failures in Early Design Stages

2020 ◽  
Vol 20 (5) ◽  
Author(s):  
Lukman Irshad ◽  
H. Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Human Error ◽  
Human Action ◽  
Time Step ◽  
Critical Function ◽  
Human Errors ◽  
Early Design ◽  
Complex Engineered Systems ◽  
Early Design Stages ◽  
Engineered Systems ◽  
Improved Performance

Abstract Human errors are attributed to a majority of accidents and malfunctions in complex engineered systems. The human error and functional failure reasoning (HEFFR) framework was developed to assess potential functional failures, human errors, and their propagation paths during early design stages so that more reliable systems with improved performance and safety can be designed. In order to perform a comprehensive analysis using this framework, a wide array of potential failure scenarios need to be tested. Coming up with such use cases that can cover a majority of faults can be challenging for engineers. This research aims overcome this limitation by creating a use case generation technique that covers both component- and human-related fault scenarios. The proposed technique is a time-based simulation that employs a modified depth first search (DFS) to simulate events as the event propagation is analyzed using HEFFR at each time-step. The results show that the proposed approach is capable of generating a wide variety of fault scenarios involving humans and components. Out of the 15.4 million scenarios that were found to violate the critical function, two had purely human-induced faults, 163,204 had purely non-human-induced faults, and the rest had a combination of both. The results also show that the framework was able to uncover hard-to-detect scenarios such as scenarios with human errors that do not propagate to affect the system. In fact, 86% of all human action combinations with nominal human-induced component behaviors had underlying human errors.

Computational Functional Failure Analysis to Identify Human Errors During Early Design Stages

2019 ◽  
Vol 19 (3) ◽  
Author(s):  
Lukman Irshad ◽  
Salman Ahmed ◽  
H. Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Failure Analysis ◽  
Human Error ◽  
Human Action ◽  
System Level ◽  
Design Stage ◽  
Human Factors Engineering ◽  
Human Errors ◽  
Functional Failure ◽  
Early Design ◽  
Early Design Stages

Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced functional failure identification and propagation (FFIP), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed toward the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. The capabilities of the proposed method is presented via a hold-up tank example, and the results are coupled with digital human modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.

Identification of Human Errors During Early Design Stage Functional Failure Analysis

2018 ◽  
Author(s):  
Lukman Irshad ◽  
Salman Ahmed ◽  
Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Failure Analysis ◽  
Human Error ◽  
System Level ◽  
Design Stage ◽  
Human Factors Engineering ◽  
Human Errors ◽  
Functional Failure ◽  
Early Design ◽  
Early Design Stage ◽  
Early Design Stages

Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced FFIP (Functional Failure Identification and Propagation), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed towards the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. To explore the capabilities of the proposed method, it is applied to a hold-up tank example and the results are coupled with Digital Human Modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.

Quantifying the Combined Effects of Human Errors and Component Failures

2021 ◽  
pp. 1-18 ◽  
Author(s):  
Lukman Irshad ◽  
Daniel Hulse ◽  
Onan Demirel ◽  
Irem Tumer ◽  
David Jensen
Keyword(s):  
Human Error ◽  
Cost Model ◽  
Cost Models ◽  
Worst Case ◽  
Human Errors ◽  
Complex Engineered Systems ◽  
Complex Interactions ◽  
Engineered Systems ◽  
Independent Effects ◽  
Component Failures

Abstract While a majority of accidents and malfunctions in complex engineered systems are attributed to human error, a closer inspection would reveal that such mishaps often emerge as a result of complex interactions between the human- and component-related vulnerabilities. To fully understand and mitigate potential risks, the effects of such interactions between component failures and human errors (in addition to their independent effects) need to be considered early. Specifically, to facilitate risk-based design, severity of such failures need to be quantified early in the design process to determine overall risk and prioritize the most important hazards. However, existing risk assessment methods either quantify the risk of component failures or human errors in isolation or are only applicable during later design stages. This work intends to overcome this limitation by introducing an expected cost model to the Human Error and Functional Failure Reasoning (HEFFR) framework to facilitate the quantification of the effects of human error and component failures acting in tandem. This approach will allow designers to assess the risk of hazards emerging from human- and component-related failures occurring in combination and identify worst-case fault scenarios. A coolant tank case study is used to demonstrate this approach. The results show that the proposed approach can help designers quantify the effects of human error and component failures acting alone and in tandem, identify and prioritize worst-case scenarios, and improve human-product interactions. However, the underlying likelihood and cost models are subject to uncertainties which may affect the assessments.

Using Automated Use Case Generation for Early Design Stage Functional Failure and Human Error Analysis

2019 ◽  
Author(s):  
Lukman Irshad ◽  
H. Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Human Error ◽  
Large Scale ◽  
Design Stage ◽  
Test Case ◽  
Use Case ◽  
Human Errors ◽  
Functional Failure ◽  
Early Design ◽  
Early Design Stage ◽  
Complex Engineered Systems

Abstract Human errors and poor ergonomics are attributed to a majority of large-scale accidents and malfunctions in complex engineered systems. Human Error and Functional Failure Reasoning (HEFFR) is a framework developed to assess potential functional failures, human errors, and their propagation paths during early design stages so that more reliable systems with improved performance and safety can be designed. In order to perform a comprehensive analysis using this framework, a wide array of potential failure scenarios need to be tested. Coming up with such use cases that can cover a majority of faults can be challenging or even impossible for a single engineer or a team of engineers. In the field of software engineering, automated test case generation techniques have been widely used for software testing. This research explores these methods to create a use case generation technique that covers both component-related and human-related fault scenarios. The proposed technique is a time based simulation that employs a modified Depth First Search (DFS) algorithm to simulate events as the event propagation is analyzed using HEFFR at each timestep. This approach is applied to a hold-up tank design problem and the results are analyzed to explore the capabilities and limitations.

The Human Error and Functional Failure Reasoning Framework: How Does It Scale?

2021 ◽  
Author(s):  
Lukman Irshad ◽  
H. Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Human Error ◽  
Risk Assessments ◽  
Modular Approach ◽  
Human Errors ◽  
Functional Failure ◽  
Computational Costs ◽  
Complex Engineered Systems ◽  
Assessment Approach ◽  
Engineered Systems ◽  
And Performance

Abstract The goal of this research is to demonstrate the applicability of the Human Error and Functional Failure Reasoning (HEFFR) framework to complex engineered systems. Human errors are cited as a root cause of a majority of accidents and performance losses in complex engineered systems. However, a closer look would reveal that such mishaps are often caused by complex interactions between human fallibilities, component vulnerabilities, and poor design. Hence, there is a growing call for risk assessments to analyze human errors and component failures in combination. The HEFFR framework was developed to enable such combined risk assessments. Until now, this framework has only been applied to simple problems, and it is prone to be computationally heavy as complexity increases. In this research, we introduce a modular HEFFR assessment approach as means of managing the complexity and computational costs of the HEFFR simulations of complex engineered systems. Then, we validate the proposed approach by testing the consistency of the HEFFR results between modular and integral assessments and between different module partitioning assessments. Next, we perform a risk assessment of a train locomotive using the modular approach to demonstrate the applicability of the HEFFR framework to complex engineered systems. The results show that the proposed modular approach can produce consistent results while reducing complexity and computational costs. Also, the results from the train locomotive HEFFR analysis show that the modular assessments can be used to produce risk insights similar to integral assessments but with a modular context.

scholarly journals fmdtools: A Fault Propagation Toolkit for Resilience Assessment in Early Design

2020 ◽  
Author(s):  
Daniel Hulse ◽  
Hannah Walsh ◽  
Andy Dong ◽  
Christopher Hoyle ◽  
Irem Tumer ◽  
...  
Keyword(s):  
Hazard Analysis ◽  
Early Design ◽  
Reliability Based Design ◽  
Complex Engineered Systems ◽  
Fault Modelling ◽  
Aerospace Systems ◽  
Engineered Systems ◽  
Recovery Schemes ◽  
Analysis Environment

Incorporating resilience in design is important for the long-term viability of complex engineered systems. Complex aerospace systems, for example, must ensure safety in the event of hazards resulting from part failures and external circumstances while maintaining efficient operations. Traditionally, mitigating hazards in early design has involved experts manually creating hazard analyses in a time-consuming process that hinders one's ability to compare designs. Furthermore, as opposed to reliability-based design, resilience-based design requires using models to determine the dynamic effects of faults to compare recovery schemes. Models also provide design opportunities, since models can be parameterized and optimized and because the resulting hazard analyses can be updated iteratively. While many analysis frameworks have been presented for early hazard assessment, these frameworks are difficult to apply without reference implementations, and most currently-available fault modelling tools are meant for the later stages of design. This paper describes fmdtools, a Python-based resilience-based design and analysis environment that solves these problems by enabling the designer to represent the system in the early design process, simulate the effects of faults, and quantify corresponding resilience metrics. This toolkit is then demonstrated in the hazard analysis and architecture design of a multi-rotor drone.

A Survey: Towards Understanding Emergent Behavior in Complex Engineered Systems

2017 ◽  
Author(s):  
Nicolás F. Soria Zurita ◽  
Irem Y. Tumer
Keyword(s):  
Research Literature ◽  
Emergent Behavior ◽  
Causal Process ◽  
Research Areas ◽  
Complex Engineered Systems ◽  
Current State ◽  
Distinctive Aspect ◽  
Early Design Stages ◽  
Engineered Systems ◽  
The Individual

The design of complex engineered systems is challenging, especially in early design stages due to the complex emergent behavior that often results in unforeseen failures. Emergent behavior is a distinctive aspect of systems in which the exhibited behavior of the system is more complex than the behavior of the individual components that shape the system. Understanding the emergent behavior is critical to perform an accurate assessment of the designed system. The objective of this paper is to explore the different existing concepts, methods, and approaches used by researchers to understand and manage emergent behavior in complex systems. We provide a critical review of the emergence concept to discern what characteristics about the causal process it reflects, so it can be used or implemented in further research in complex engineered systems. Specifically, this research explores the current state of-the-art on emergence, and identifies possible gaps in the research literature. We present different approaches used by engineers to deal with emergent behavior in different research areas such as Multiagent Systems (MAS), System of Systems (SOS), and Emergence Engineering.

Identification of Human–System Interaction Errors During Early Design Stages Using a Functional Basis Framework

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Nicolás F. Soria Zurita ◽  
Robert B. Stone ◽  
H. Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Design Process ◽  
Human Error ◽  
Failure Modes ◽  
Composite Function ◽  
Human System ◽  
Early Design ◽  
Functional Basis ◽  
Potential Failure ◽  
Early Design Stages ◽  
Criticality Analysis

Abstract Engineers have developed different design methodologies capable of identifying failure modes of engineering systems. The most common methods used in industry are failure modes and effects analysis, and failure modes effects and criticality analysis. Nevertheless, such methodologies have a significant limitation regarding incorporating the final user in the analysis and are not suited to identifying potential failure modes caused by physical human–system interactions. Engineering methods usually have a lack of sufficient attention to human–system interactions during the early design stages, even though introducing human factors principles is recognized as an essential analysis during the design process. As a result, designers rely on developing detailed and expensive physical or virtual prototypes to evaluate physical human–system interactions and identify potential failure modes caused by such interactions incorporating design modifications after a prototype is developed can be time-consuming, costly, and if significant changes are needed, the entire prototype requires to be constructed again. Identifying system–user interactions and possible failure modes associated with such interactions before developing a prototype can significantly improve the design process. In previous work, the authors introduced the function–human error design method (FHEDM), a tool capable of distinguishing possible human–system interaction failure modes using a functional basis framework. In this work, we examined the implementation of FHEDM within 148 products extracted from the design repository. The results are grouped in the composite function–user interaction error (FUIE) matrix, which can be used as a preliminary design database presenting information regarding the possible human error present in function-flow combinations.

scholarly journals Fmdtools

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Daniel Hulse ◽  
Hannah Walsh ◽  
Andy Dong ◽  
Christopher Hoyle ◽  
Irem Tumer ◽  
...  
Keyword(s):  
Hazard Analysis ◽  
Theoretical Frameworks ◽  
Early Design ◽  
Reliability Based Design ◽  
Complex Engineered Systems ◽  
Aerospace Systems ◽  
Engineered Systems ◽  
Recovery Schemes ◽  
Early Phases

Incorporating resilience in design is important for the long-term viability of complex engineered systems. Complex aerospace systems, for example, must ensure safety in the event of hazards resulting from part failures and external circumstances while maintaining efficient operations. Traditionally, mitigating hazards in early design has involved experts manually creating hazard analyses in a time-consuming process that hinders one’s ability to compare designs. Furthermore, as opposed to reliability-based design, resilience-based design requires using models to determine the dynamic effects of faults to compare recovery schemes. Models also provide design opportunities, since models can be parameterized and optimized and because the resulting hazard analyses can be updated iteratively. While many theoretical frameworks have been presented for early hazard assessment, most currently-available modelling tools are meant for the later stages of design. Given the wide adoption of Python in the broader research community, there is an opportunity to create an environment for researchers to study the resilience of different PHM technologies in the early phases of design. This paper describes fmdtools, an attempt to realize this opportunity with a set of modules which may be used to construct different design models, simulate system behaviors over a set of fault scenarios and analyze the resilience of the resulting simulation results. This approach is demonstrated in the hazard analysis and architecture design of a multi-rotor drone, showing how the toolkit enables a large number of analyses to be performed on a relatively simple model as it progresses through the early design process.

The Unreasonable Ineptitude of Deep Image Classification Networks

2020 ◽  
Vol 64 (1) ◽  
pp. 410-414
Author(s):  
Shane T. Mueller ◽  
Priyansh Agarwal ◽  
Anne Linja ◽  
Nisarg Dave ◽  
Lamia Alam
Keyword(s):  
Image Classification ◽  
Human Error ◽  
Academic Community ◽  
Training Set ◽  
Human Errors ◽  
Human Recognition ◽  
Deep Image ◽  
Improved Performance ◽  
Almost All ◽  
Popular Image

The success of deep image classification networks has been met with enthusiasm and investment from both the academic community and industry. We hypothesize users will expect these systems to behave similarly to humans, and to succeed and fail in ways humans do. To investigate this, we tested six popular image classifiers on imagery from ten tool categories, examining how 17 visual transforms impacted both human and AI classification. Results showed that (1) none of the visual transforms we examined produced substantial impairment for human recognition; (2) human errors were limited to mostly to functional confusions; (3) almost all visual transforms impacted nearly every image classifier negatively and often catastrophically; (4) human expectations about performance of AI classifiers map more closely onto human error than AI performance; and (5) models trained with an enriched training set involving examples of the transformed imagery achieved improved performance but were not inoculated from error.

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