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.

Introducing Likelihood of Occurrence and Expected Cost to Human Error and Functional Failure Reasoning Framework

2020 ◽  
Author(s):  
Lukman Irshad ◽  
Daniel Hulse ◽  
H. Onan Demirel ◽  
Irem Y. Tumer ◽  
David C. Jensen
Keyword(s):  
Human Error ◽  
Cost Model ◽  
Critical Event ◽  
Cost Models ◽  
Worst Case ◽  
Expected Cost ◽  
Human Errors ◽  
Functional Failure ◽  
System Risk ◽  
Component Failures

Abstract Risk-based design uses severity and occurrence quantification to determine overall system risk and prioritize the most important hazards. To fully understand and effectively mitigate potential risks, the effects of component failures and human errors (acting alone and in tandem) need to be considered early. Then one can determine whether to allocate resources to proactively mitigate human errors in the design process. In previous work, the Human Error and Functional Failure Reasoning (HEFFR) framework was developed to model effects of human errors and component failures in a system, taking critical event scenarios as inputs and producing functional failures, human errors, and their propagation paths as outputs. With automated scenario generation, this framework can model millions of scenarios that cause system critical functions to fail. However, the outputs of this framework do not include any quantifiable measures to assess the risk of the hazards or prioritize fault scenarios. This work addresses these shortcomings by using a scenario probability and cost model to quantify the expected cost of failures in the HEFFR framework. A coolant tank case study is used to demonstrate this approach. The results show that the quantifiable measures enable HEFFR to identify worst-case scenarios, prioritize scenarios with the highest impact, and improve human-product interactions. However, the underlying likelihood and cost models are subject to uncertainties which may affect the assessments.

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.

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.

Using Rio-Paris Flight 447 Crash to Assess Human Error and Failure Propagation Analysis Early in Design

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Lukman Irshad ◽  
H. Onan Demirel ◽  
Irem Y. Tumer ◽  
Guillaume Brat
Keyword(s):  
Human Error ◽  
Prediction Algorithm ◽  
Engineering System ◽  
Human Errors ◽  
System Failures ◽  
Propagation Analysis ◽  
Failure Propagation ◽  
Complex Engineering ◽  
Component Failures ◽  
Complex Engineering System

Abstract While a majority of system vulnerabilities such as performance losses and accidents are attributed to human errors, a closer inspection would reveal that often times the accumulation of unforeseen events that include both component failures and human errors contribute to such system failures. Human error and functional failure reasoning (HEFFR) is a framework to identify potential human errors, functional failures, and their propagation paths early in design so that systems can be designed to be less prone to vulnerabilities. In this paper, the application of HEFFR within the complex engineering system domain is demonstrated through the modeling of the Air France 447 crash. Then, the failure prediction algorithm is validated by comparing the outputs from HEFFR and what happened in the actual crash. Also, two additional fault scenarios are executed within HEFFR and in a commercially available flight simulator separately, and the outcomes are compared as a supplementary validation.

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.

Assessment of Human Reliability Under the Conditions of Uncertainty: SPAR-H Methodology Interpreted in Terms of Interval-Valued Probabilities

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Victor G. Krymsky ◽  
Farit M. Akhmedzhanov
Keyword(s):  
Human Error ◽  
Human Reliability ◽  
Maximum Probability ◽  
Worst Case ◽  
Human Errors ◽  
Performance Shaping Factors ◽  
Error Probabilities ◽  
Available Information ◽  
Interval Valued ◽  
Human Error Probability

Abstract The well-known standardized plant analysis risk-human reliability (SPAR-H) methodology is widely used for analysis of human reliability in complex technological systems. It allows assessing the human error probability taking into account eight important groups of performance shaping factors. Application of this methodology to practical problems traditionally involves assumptions which are difficult to verify under the conditions of uncertainty. In particular, it introduces only two possible values of the nominal human error probabilities (for diagnosis and for actions) which do not cover the whole spectrum of the tasks within operator's activity. In addition, although the traditional methodology considers the probabilities of human errors as the random variables, it operates only on a single predefined type of distribution for these variables and does not deal with the real situations in which the type of distribution remains uncertain. The paper proposes modification to the classical approach to enable more adequate modeling of real situations with the lack of available information. The authors suggest usage of the interval-valued probability technique and of the expert judgment on the maximum probability density for actual probabilities of human errors. Such methodology allows obtaining generic results that are valid for the entire set of possible distributions (not only for one of them). The modified methodology gives possibility to derive final assessments of human reliability in interval form indicating “the best case” and “the worst case.” A few numerical examples illustrate the main stages of the suggested procedure.

scholarly journals Comparison of four major industrial disasters from the perspective of human error factor

2020 ◽  
Vol 305 ◽  
pp. 00017
Author(s):  
Doru Costin Darabont ◽  
Daniel Onut Badea ◽  
Alina Trifu
Keyword(s):  
Human Factors ◽  
Human Error ◽  
Technological Advancement ◽  
Human Errors ◽  
Occupational Risks ◽  
Factors Analysis ◽  
Error Factor ◽  
Swiss Cheese Model ◽  
Component Failures ◽  
Cheese Model

This paper presents the preliminary findings of a project still in progress at INCDPM regarding” Knowledge transfer partnership and research development in the assessment and prevention of occupational risks which may conduct to disaster”. After studying the major industrial disasters of our times, it become clear that even with technological advancement, human error is still the major cause of accidents and incidents. Analysis of human error and their role in accidents is an important part of developing systematic methods for reliability in the industry and risk prediction. To obtain data for predictive analysis is necessary to analyse accidents and incidents to identify its causes in terms of component failures and human errors. Therefore, a proper understanding of human factors in the workplace is an important aspect in the prevention of accidents, and human factors should be considered in any program to prevent those that are caused by human error. The comparison between four major industrial disasters (Chernobyl, Bhopal, Deepwater Horizon, Alpha Piper) was made using Human Factors Analysis and Classification System (HFACS), a modified version of “Swiss Cheese” model that describes the levels at which active failures and latent failures/conditions may occur within complex operations.

Causes of Structural Failures with Steel Structures

2017 ◽  
Author(s):  
Goran Alpsten
Keyword(s):  
Structural Damage ◽  
Human Error ◽  
Structural Reliability ◽  
Steel Structures ◽  
Fatigue Loading ◽  
Structural Instability ◽  
Human Errors ◽  
Collapse Mode ◽  
Load Carrying ◽  
Structural Failures

This paper is based on the experience from investigating over 400 structural collapses, incidents and serious structural damage cases with steel structures which have occurred over the past four centuries. The cause of the failures is most often a gross human error rather than a combination of “normal” variations in parameters affecting the load-carrying capacity, as considered in normal design procedures and structural reliability analyses. Human errors in execution are more prevalent as cause for the failures than errors in the design process, and the construction phase appears particularly prone to human errors. For normal steel structures with quasi-static (non-fatigue) loading, various structural instability phenomena have been observed to be the main collapse mode. An important observation is that welds are not as critical a cause of structural steel failures for statically loaded steel structures as implicitly understood in current regulations and rules for design and execution criteria.

A Method for Marine Human Error Probability Estimate: APJE-SLIM

2011 ◽  
Vol 97-98 ◽  
pp. 825-830 ◽  
Author(s):  
Yong Tao Xi ◽  
Chong Guo
Keyword(s):  
Collision Avoidance ◽  
Human Error ◽  
Shipping Industry ◽  
Human Reliability ◽  
Combined Method ◽  
Probability Estimate ◽  
Human Errors ◽  
Performance Shaping Factors ◽  
Industry Research ◽  
Human Error Probability

Safety is the eternal theme in shipping industry. Research shows that human error is the main reason of maritime accidents. Therefore, it is very necessary to research marine human errors, to discuss the contexts which caused human errors and how the contexts effect human behavior. Based on the detailed investigation of human errors in collision avoidance behavior which is the most key mission in navigation and the Performance Shaping Factors (PSFs), human reliability of mariners in collision avoidance was analyzed by using the integration of APJE and SLIM. Result shows that this combined method is effective and can be used for the research of maritime human reliability.

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.

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