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

2021 ◽  
Author(s):  
Lukman Irshad ◽  
Daniel Hulse ◽  
H. Onan Demirel ◽  
Irem Y. Tumer ◽  
David C. Jensen
Author(s):  
Lukman Irshad ◽  
Daniel Hulse ◽  
H. Onan Demirel ◽  
Irem Y. Tumer ◽  
David C. Jensen

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.


Author(s):  
Lukman Irshad ◽  
Salman Ahmed ◽  
Onan Demirel ◽  
Irem Y. Tumer

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.


Author(s):  
Lukman Irshad ◽  
Salman Ahmed ◽  
H. Onan Demirel ◽  
Irem Y. Tumer

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.


Author(s):  
Lukman Irshad ◽  
H. Onan Demirel ◽  
Irem Y. Tumer

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.


2021 ◽  
Author(s):  
Lukman Irshad ◽  
H. Onan Demirel ◽  
Irem Y. Tumer

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.


Author(s):  
Ryoichi Hamazaki ◽  
Kazunori Hashimoto ◽  
Takayoshi Kusunoki ◽  
Chikahiro Satou

In this paper, we introduce the overview of the requirements and the complementary information on the evaluation of containment functional failure frequency (CFF) in the revised version of “A Standard for Procedures of Probabilistic Risk Assessment of Nuclear Power Plants during Power Operation (Level 2 PRA) “[1] in Japan, which was developed and revised at the Level 2 PRA Subcommittee under the Atomic Energy Society of Japan (AESJ). Although the Level 2 PRA standard includes the evaluation of CFF and radiological source terms, we explain only the evaluation of CFF in this paper. In the evaluation of CFF, the physical response analysis and the probabilistic analysis are included as follows. The accident progression analysis is performed for each of the plant damage states, considering the operation status of mitigation systems, thermal-hydraulic behavior and core damage progression, and occurrences of some key events such as reactor pressure vessel failure. The containment event tree (CET) is developed classifying the accident progress in tree diagram. In the CET, some headings are arranged sequentially considering the accident progression. The headings correspond to the phenomena occurrence and the systems operation status, and a branch probability is assigned at each branch of heading. The branch probabilities of the phenomena are evaluated by either the Risk Oriented Accident Analysis Methodology (ROAAM) or the Decomposition Event Tree (DET) analysis considering the containment threats. The branch probabilities on the phenomena are set as the probability distributions, because the phenomena and the analysis have uncertainties. The branch probabilities on the systems operation are evaluated using the fault tree analysis and human error analysis. The containment functional failure modes are assigned at the end state of the CET considering the type of load against containment integrity. For the evaluation of the non-energetic load, the integral codes such as MELCOR [2], THALES-2 [3], and MAAP4 [4] etc. are used. On the other hand, various mechanistic codes are used for the evaluation of energetic phenomena such as steam explosion. The containment functional failure is judged by comparing the ultimate strength or the fragility of containment structure and the generated loads. After all, the CFF can be obtained by summing the frequency of containment functional failure mode. In the Level 2 PRA standard in Japan, the requirements in each evaluation process above are described. In addition, the technical background and the examples as the complementary information on each requirement are described in the Annex of the standard to help the application of the standard. In this revision, the body is revised to clarify the requirements on the quantification of the CET. The Annex is revised to incorporate the up-to-date information on severe accident research and severe accident management (SAM) measures. The updated information includes the melt stratification (OECD/MASCA project [5]), the steam explosion (SERENA project [6] and PULiMS/SES experiments [7]), the ex-vessel debris coolability (OECD/MCCI project [8]), debris jet breakup, the melt spreading, the coolability of the particulate bed, and the containment vessel (CV) fragility evaluation. Some future challenges are extracted from the lessons learned from the Fukushima Daiichi accident, such as development of the Level 2 PRA for the external hazard as earthquake and tsunami, quantification of impact on the containment integrity of hydrogen detonation in the adjacent buildings, and human error evaluation in the external hazard.


2020 ◽  
Vol 10 (2) ◽  
pp. 103-111
Author(s):  
Andrey K. Babin ◽  
Andrew R. Dattel ◽  
Margaret F. Klemm

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.


2006 ◽  
Author(s):  
Larry Bailey ◽  
Julia Pounds ◽  
Carol Manning ◽  
David Schroeder

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