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 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.

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.

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.

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.

scholarly journals Human errors in traffic accidents: differences between car drivers and motorcyclists’ experience

2019 ◽  
Vol 2 (2) ◽  
pp. 118
Author(s):  
Guritnaningsih P Santoso ◽  
Dewi Maulina
Keyword(s):  
Psychological Factors ◽  
Traffic Accident ◽  
Human Error ◽  
Traffic Accidents ◽  
Causes Of Death ◽  
Human Errors ◽  
Police Records ◽  
And Performance ◽  
Recognition Errors ◽  
Decision Errors

Traffic accidents have become one of the main causes of death in Indonesia. The biggest contributor to traffic accidents are motorcyclists. According to police records, human error plays a major role in the occurrence of accidents. The aim of this study is to analyze the potential types of human error that contribute to traffic accidents, as well as the psychological factors that underlie traffic accidents experienced by car drivers and motorcyclists. Data was collected by interviewing five car drivers and five motorcyclists. Results show that the car drivers tend to perform a type of human error which is classified as lapses, while the motorcyclists tend to do an error of slips. For psychological factors that underlie traffic accident, results show that both car drivers and motorcyclists made recognition errors, i.e. did not estimate distance, time, and speed. They also made decision errors, i.e. did not avoid the situation immediately, and performance errors, i.e. a motorcyclist stepped on the gas pedal by mistake. Other errors done by the car drivers were being sleepy and drunk, whereas other errors done by motorcyclists were not having a riding license and feeling tired. The implication of this study is to make the drivers/riders aware of the importance of cognitive aspects in driving.

scholarly journals Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

2021 ◽  
Vol 11 (2) ◽  
pp. 749
Author(s):  
Yaniel Torres ◽  
Sylvie Nadeau ◽  
Kurt Landau
Keyword(s):  
Human Error ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Manual Assembly ◽  
Human Errors ◽  
Assembly Instructions ◽  
Inspection Process ◽  
And Performance ◽  
Error Probabilities ◽  
Assembly Guidance

Manual assembly operations are sensitive to human errors that can diminish the quality of final products. The paper shows an application of human reliability analysis in a realistic manufacturing context to identify where and why manual assembly errors occur. The techniques SHERPA and HEART were used to perform the analysis of human reliability. Three critical tasks were selected for analysis based on quality records: (1) installation of three types of brackets using fasteners, (2) fixation of a data cable to the assembly structure using cushioned loop clamps and (3) installation of cap covers to protect inlets. The identified error modes with SHERPA were: 36 action errors, nine selection errors, eight information retrieval errors and six checking errors. According to HEART, the highest human error probabilities were associated with assembly parts sensitive to geometry-related errors (brackets and cushioned loop clamps). The study showed that perceptually engaging assembly instructions seem to offer the highest potential for error reduction and performance improvement. Other identified areas of action were the improvement of the inspection process and workers’ provision with better tracking and better feedback. Implementation of assembly guidance systems could potentially benefit worker’s performance and decrease assembly errors.

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.

Toward an Automated Model-Based Geometric Method of Representing Function Failure Propagation Across Uncoupled Systems

2014 ◽  
Author(s):  
Isaac J. Ramp ◽  
Douglas L. Van Bossuyt
Keyword(s):  
Functional Model ◽  
Geometric Method ◽  
Flow Paths ◽  
Functional Failure ◽  
Model Based ◽  
Complex Engineered Systems ◽  
Minimum Number ◽  
Failure Propagation ◽  
Functional Layout ◽  
Engineered Systems

The complex engineered systems being designed today must rapidly and accurately be developed to satisfy customer needs while accomplishing required functions with a minimum number of failures. Failure analysis in the conceptual stage of design has expanded in recent years to account for failures in functional modeling. However, function failure propagation across normally uncoupled functions and subsystems has not been fully addressed. A functional model-based geometric method of predicting and mitigating functional failure propagation across systems, which are uncoupled during nominal use cases, is presented. Geometric relationships between uncoupled functions are established to serve as failure propagation flow paths. Mitigation options are developed based upon the geometric relationships and a path toward physical functional layout is provided to limit failure propagation across uncoupled subsystems. The model-based geometric method of predicting and mitigating functional failure propagation across uncoupled engineered systems guides designers toward improved protection and isolation of cross-subsystem failure propagation.

An Alternative Method for Identifying a Failed Engine in Twin-Engine Propeller Aircraft

2020 ◽  
Vol 10 (2) ◽  
pp. 103-111
Author(s):  
Andrey K. Babin ◽  
Andrew R. Dattel ◽  
Margaret F. Klemm
Keyword(s):  
Human Error ◽  
Flight Simulator ◽  
Traditional Group ◽  
Aircraft Accidents ◽  
Alternative Method ◽  
Indicator Group ◽  
Group A ◽  
The Dead ◽  
Engine Failure ◽  
And Performance

Abstract. Twin-engine propeller aircraft accidents occur due to mechanical reasons as well as human error, such as misidentifying a failed engine. This paper proposes a visual indicator as an alternative method to the dead leg–dead engine procedure to identify a failed engine. In total, 50 pilots without a multi-engine rating were randomly assigned to a traditional (dead leg–dead engine) or an alternative (visual indicator) group. Participants performed three takeoffs in a flight simulator with a simulated engine failure after rotation. Participants in the alternative group identified the failed engine faster than the traditional group. A visual indicator may improve pilot accuracy and performance during engine-out emergencies and is recommended as a possible alternative for twin-engine propeller aircraft.

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