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.

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.

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.

scholarly journals Application of Integrated Human Error Management in Human Factors Engineering Process to Nuclear Power Plant Design

2021 ◽  
Vol 6 (4) ◽  
pp. 1186-1198
Author(s):  
Kenji Mashio ◽  
Kodo Ito
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Human Factors ◽  
Nuclear Power ◽  
Human Error ◽  
Error Management ◽  
Human Factors Engineering ◽  
Plant Design ◽  
Integrated Process ◽  
Human Errors

Integrated process of human error management in human factors engineering (HFE) process provides a systematic direction for the design countermeasures development to prevent potential human errors. The process analyzes performance influence factors (PIFs) for crew failure modes (CFMs) and human failure events (HFEvs) in human reliability analysis (HRA). This paper provides applications of the process to the event evaluation for nuclear power plant design, especially PWR. In this application, the HRA/HFE integrated process had specified further detail for PIF attributes which had not been obtained in HRA, and showed further investigations to treat how operators induced their human errors through their cognitive task process in their work environment. This application showed effectiveness of the process in order to provide design countermeasures for preventing potential human errors occurrence based on the extensive PIFs and their error forcing context in HRA.

An Association Rule Approach for Identifying Physical System-User Interactions and Potential Human Errors Using a Design Repository

2019 ◽  
Author(s):  
Nicolás F. Soria Zurita ◽  
Melissa Anne Tensa ◽  
Vincenzo Ferrero ◽  
Robert B. Stone ◽  
Bryony DuPont ◽  
...  
Keyword(s):  
Data Mining ◽  
Design Process ◽  
Human Error ◽  
Failure Modes ◽  
Functional Model ◽  
Design Stage ◽  
User Interactions ◽  
Early Design ◽  
Design Information ◽  
Rule Approach

Abstract During the design process, designers must satisfy customer needs while adequately developing engineering objectives. Among these engineering objectives, human considerations such as user interactions, safety, and comfort are indispensable during the design process. Nevertheless, traditional design engineering methodologies have significant limitations incorporating and understanding physical user interactions during early design phases. For example, Human Factors methods use checklists and guidelines applied to virtual or physical prototypes at later design stages to evaluate the concept. As a result, designers struggle to identify design deficiencies and potential failure modes caused by user-system interactions without relying on the use of detailed and costly prototypes. The Function-Human Error Design Method (FHEDM) is a novel approach to assess physical interactions during the early design stage using a functional basis approach. By applying FHEDM, designers can identify user interactions required to complete the functions of the system and to distinguish failure modes associated with such interactions, by establishing user-system associations using the information of the functional model. In this paper, we explore the use of data mining techniques to develop relationships between component, functions, flows and user interactions. We extract design information about components, functions, flows, and user interactions from a set of distinct coffee makers found in the Design Repository to build associations rules. Later, using a functional model of an electric kettle, we compared the functions, flows, and user interactions associations generated from data mining against the associations created by the authors, using the FHEDM. The results show notable similarities between the associations built from data mining and the FHEDM. We are suggesting that design information from a rich dataset can be used to extract association rules between functions, flows, components, and user interactions. This work will contribute to the design community by automating the identification of user interactions from a functional model.

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.

Capturing Interactions and Emergent Failure Behavior in Complex Engineered Systems at Multiple Scales

2011 ◽  
Author(s):  
Nikolaos Papakonstantinou ◽  
Seppo Sierla ◽  
David C. Jensen ◽  
Irem Y. Tumer
Keyword(s):  
Complex System ◽  
System Design ◽  
Emergent Behavior ◽  
System Level ◽  
Design Stage ◽  
Failure Behavior ◽  
Early Design ◽  
Large Complex ◽  
Large Complex System ◽  
Complex System Design

Large complex systems exhibit complex nominal and failure behavior and understanding that behavior is critical to the accurate assessment of risk. However, this assessment is difficult to accomplish in the early design stage. Multiple subsystem interactions and emergent behavior further complicate early design risk analysis. The goal of this paper is to demonstrate necessary modifications of an existing function-based failure assessment tool for application to the large complex system design domain. Specifically, this paper demonstrates how specific adaptations to this early, qualitative approach to system behavioral simulation and analysis help overcome some of the challenges to large complex system design. In this paper, a boiling water nuclear reactor design serves as a motivating case study for showing how this approach can capture complex subsystem interactions, identify emergent behavior trends, and assess failures at both the component and system level.

scholarly journals Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages

2021 ◽  
Vol 8 (2) ◽  
pp. 270-283
Author(s):  
Hanieh Nourkojouri ◽  
◽  
Nastaran Seyed Shafavi ◽  
Mohammad Tahsildoost ◽  
Zahra Sadat Zomorodian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Absolute Error ◽  
Simulation Software ◽  
Machine Learning Algorithms ◽  
Evaluation Framework ◽  
Design Stage ◽  
Visual Comfort ◽  
Early Design ◽  
Early Design Stages

Application of machine learning methods as an alternative for building simulation software has been progressive in recent years. This research is mainly focused on the assessment of machine learning algorithms in prediction of daylight and visual comfort metrics in the early design stages and providing a framework for the required analyses. A dataset was primarily derived from 2880 simulations developed from Honeybee for Grasshopper. The simulations were conducted for a side-lit shoebox model. The alternatives emerged from different physical features, including room dimensions, interior surfaces’ reflectance factor, window dimensions, room orientations, number of windows, and shading states. Five metrics were applied for daylight evaluations, including useful daylight illuminance, spatial daylight autonomy, mean daylight autonomy, annual sunlit exposure, and spatial visual discomfort. Moreover, view quality was analyzed via a grasshopper-based algorithm, developed from the LEED v4 evaluation framework. The dataset was further analyzed with an artificial neural network algorithm. The proposed predictive model had an architecture with a single hidden layer consisting of 40 neurons. The predictive model learns through a trial and error method with the aid of loss functions of mean absolute error and mean square error. The model was further analyzed with a new set of data for the validation process. The accuracy of the predictions was estimated at 97% on average. The View range metric in the quality view assessment, mean daylight autonomy and useful daylight illuminance had the best prediction accuracy among others respectively. The developed model which is presented as a framework could be used in early design stage analyses without the requirement of time-consuming simulations.

scholarly journals Patient Safety and the Significance of Human Factors Engineering Modality: A Review

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Bashkin Osnat
Keyword(s):  
Patient Safety ◽  
Human Factors ◽  
Human Error ◽  
Human Factors Engineering ◽  
Human Beings ◽  
Medical Systems ◽  
Human Errors ◽  
Proactive Approach ◽  
Depth Analysis ◽  
The Many

The issue of patient safety and medical human error has been arousing growing concern around the world. Attempts to reduce the rate of human error present a great challenge, and there is an increased understanding that the issue of patient safety in healthcare systems is a complex one that requires in-depth analysis and understanding. Despite the many programs and interventions designed to reduce the rate of human medical errors, various publications that expose the extent of this phenomenon point to a high percentage of human errors that causes injury, and to the difficulties in improving patient safety. The understanding that the focus must be on prevention and the growing need for practical solutions have led to the involvement of disciplines such as human-factors engineering in an attempt to understand the root causes of safety problems and find ways to prevent them. Human-factors engineering is a proactive approach that may contribute to the planning of safe medical systems by taking into account the diverse needs, capabilities, and limitations of the human beings involved in these systems. This article reviews the benefits and challenges in applying the principles of human-factors engineering to promote patient safety, as well as the implications for policy in the field

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