Error Probabilities and Relationships in Assembly and Maintenance of Aircraft Engines

2018 ◽  
Vol 62 (1) ◽  
pp. 1599-1603 ◽  
Author(s):  
Brandon D. Richards
Keyword(s):  
Human Error ◽  
Aircraft Engine ◽  
Error Assessment ◽  
Aircraft Engines ◽  
Common Causes ◽  
Analysis System ◽  
Error Probabilities ◽  
Engine Maintenance ◽  
Human Error Probability

Aircraft engine maintenance errors recorded during commercial revenue operation (field operation) share common causes and outcomes as those documented in original equipment manufacturer (OEM) and maintenance repair and overhaul (MRO) facilities. Utilizing data from one or more one of these sources can increase the understanding of skill-based, decision and perceptual errors occurring during assembly, operation and maintenance. The null hypothesis in this descriptive, explanatory research is: the nominal human error probability of removing and replacing a specified line replaceable unit (LRU)/component in field operations and OEM/MRO is the same. Quantitative analysis will include probabilities of human error for specific processes in both field operations and MRO/OEM environments determined by the Human Error Assessment and Reduction Technique (HEART). Qualitative analysis will include the classification of past errors utilizing the Human Factors Classification and Analysis System (HFACS). Accounting for all available types of data enables more precise and appropriate corrective actions.

A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

2020 ◽  
Vol 64 (1) ◽  
pp. 129-133
Author(s):  
Katherine Darveau ◽  
Daniel Hannon ◽  
Chad Foster
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Human Error ◽  
Data Science ◽  
Aircraft Engine ◽  
Rule Based ◽  
Root Cause ◽  
Textual Data ◽  
Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

scholarly journals HUMAN ERROR IDENTIFICATION IN BUS DRIVER WORK USING SHERPA AND HEART

2020 ◽  
Vol 5 (2) ◽  
pp. 62-69
Author(s):  
Mitasya Susilo
Keyword(s):  
Error Probability ◽  
Human Error ◽  
Traffic Accidents ◽  
Error Assessment ◽  
Hierarchical Task Analysis ◽  
A Value ◽  
Bus Driver ◽  
Directorate General ◽  
Prediction Approach ◽  
Human Error Probability

Traffic accidents, especially with a large capacity such as bus, can be caused by several factors. According to the Indonesian Directorate General of Land Transportation of the Ministry of Transportation in 2012, the factors causing traffic accidents in Indonesia are a human factor of 93.52%, vehicle factor by 2.76%, road factor 3.23%, and environmental factor by 0.49%. Therefore, research is needed to identify which human error has the greatest probability of accident cause using Systematic Human Error Reduction and Prediction Approach (SHERPA) method to identify job desk using Hierarchical Task Analysis (HTA) and Human Error Assessment Reduction Technique (HEART) method to calculate Human Error Probability (HEP). Based on the calculation of Human Error Probability value known the highest HEP value is not running the vehicle in accordance with the provisions of the speed that has been set with 0.375. Next is not to record or forget to record the damage that occurred during the trip with a value of 0.21. It did not check Bus equipment with a HEP value of 0.19, did not report when there was a problem on the street with a HEP value of 0.18 and did not break for the next preparation for departure with a HEP value of 0.15

scholarly journals Identifikasi Human Error Pada Rantai Pasok UMKM Roti

2019 ◽  
Vol 2 (4) ◽  
Author(s):  
Dini Wahyuni ◽  
Yuli Santa Elisa Bagariang
Keyword(s):  
Supply Chain ◽  
Error Probability ◽  
Human Error ◽  
Reference Model ◽  
Small And Medium Enterprises ◽  
Reduction Technique ◽  
Error Assessment ◽  
A Value ◽  
Medium Businesses ◽  
Human Error Probability

Kesuksesan usaha mikro kecil menengah dipengaruhi oleh kegiatan-kegiatan supply chain sehingga harus terjalin hubungan baik di dalamnya. Peran manusia sebagai tenaga kerja pada usaha mikro kecil menengah dominan mempengaruhi kualitas produk yang dihasilkan, karena operator memiliki peluang melakukan kesalahan. Penelitian ini bertujuan menganalisis human error pada rantai pasok UMKM makanan di kota Medan dengan adopsi model SCOR (Supply Chain Operations Reference). Metode HEART (Human Error Assessment and Reduction Technique) digunakan untuk mengetahui human error dan tingkat probabilitas human error pada UMKM makanan. Berdasarkan hasil perhitungan nilai assessed effect (AE) diperoleh total AE terbesar pada proses make dengan nilai sebesar 58,69. Human Error Probability (HEP) terbesar terdapat pada proses pengisian adonan dengan varian rasa yang diinginkan oleh konsumen, dengan nilai Human Error Probability (HEP) sebesar 5,282. Dari hasil penelitian dapat menjadi acuan menyusun rencana perbaikan agar human error dapat tereduksi.   The success of micro, small and medium enterprises is influenced by supply chain activities, so there must be a good relationship within it. The role of humans as workers in micro, small and medium businesses predominantly affects the quality of the products produced, because operators have the opportunity to make mistakes. This study aims to analyze human error in the food MSME supply chain in Medan by adopting the SCOR (Supply Chain Operations Reference) model. The HEART (Human Error Assessment and Reduction Technique) method is used to determine human error and the level of human error probability at food MSMEs. Based on the calculation of the assessed effect (AE) value, the largest total AE was obtained in the make process with a value of 58.69. The biggest Human Error Probability (HEP) is in the process of filling the dough with the flavor variants desired by consumers, with a value of Human Error Probability (HEP) of 5.282. From the results of the study can be a reference to draw up plans for improvement so that human error can be reduced.

A Study of Expert Judgment on Human Error Probability

1988 ◽  
Vol 32 (15) ◽  
pp. 954-957
Author(s):  
Bernhard Zimolong ◽  
Barbara Stolte
Keyword(s):  
Flexible Manufacturing ◽  
Human Error ◽  
Calibration Equation ◽  
Performance Shaping Factors ◽  
Relative Contribution ◽  
Missing Data Problem ◽  
Subjective Estimation ◽  
Error Probabilities ◽  
Reliability Measurement ◽  
Human Error Probability

An experiment was conducted to derive empirically human error probabilities from a task performed under 12 different conditions. The task was to control a simulated flexible manufacturing scenario (FMS) under three Performance Shaping Factors (PSF): Incentive, workload and event frequency of breakdowns. Six experts with background in human factors assess the relative contribution of each PSF in affecting the likelihood of failure with the multi attribute decomposition technique. The conversion of the assessment values to probabilities was achieved by the use of an empirically derived calibration equation. Results indicate a poor match of empirical HEPs and their estimates and increase the doubts that subjective estimation is a solution to the missing data problem in reliability measurement.

The Use of Heart to Assess the Risk of Remote Control Locomotive Operations: A Tale of Two Cities

2007 ◽  
Vol 51 (20) ◽  
pp. 1411-1415
Author(s):  
Stephen J. Reinach ◽  
Steven Fadden ◽  
Frederick C. Gamst ◽  
Sarah A. Acton
Keyword(s):  
Remote Control ◽  
Human Error ◽  
Operating Costs ◽  
Error Assessment ◽  
Subject Matter Experts ◽  
Two Cities ◽  
Error Probabilities ◽  
Task Types ◽  
Methods Of Operation ◽  
Selection Of

In an effort to reduce operating costs and increase safety and efficiency, U.S. Class I freight railroads have begun to use remotely controlled locomotives in and around railroad switching yards. To better understand the safety implications of implementing this technology, a human reliability assessment was conducted to compare remotely controlled locomotive operations with conventional (engineer onboard) yard switching operations. This paper discusses application of the Human Error Assessment and Reduction Technique (HEART) with 2 yard switching employee subject matter experts. Each was asked to assess 11 conventional scenarios and 11 nearly-identical remote control scenarios. Human error probabilities were calculated for each scenario. The HEART assessment revealed no overall difference in human error probabilities between the 2 methods of operation. Additional analyses suggest significant variability between the two assessors. This paper explores differences in how assessors used HEART, including differences in selection of generic task types and error-producing conditions.

Human Factor Risk Assessment During Emergency Condition in Harsh Environment

2013 ◽  
Author(s):  
Mashrura Musharraf ◽  
Faisal Khan ◽  
Brian Veitch ◽  
Scott MacKinnon ◽  
Syed Imtiaz
Keyword(s):  
Risk Assessment ◽  
Human Factors ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Expert Judgment ◽  
Harsh Environment ◽  
Unrealistic Assumption ◽  
Error Probabilities ◽  
Human Error Probability

This paper presents a quantitative approach to human factors risk analysis during emergency conditions on an offshore petroleum facility located in a harsh environment. Due to the lack of human factors data for emergency conditions, most of the available human factors risk assessment methodologies are based on expert judgment techniques. Expert judgment is a valuable technique, however, it suffers from vagueness, subjectivity and incompleteness due to a lack of supporting empirical evidence. These weaknesses are often not accounted for in conventional human factors risk assessment. The available approaches also suffer from the unrealistic assumption of independence of the human performance shaping (HPS) factors and actions. The focus of this paper is to address the issue of handling uncertainty associated with expert judgments and to account for the dependency among the HPS factors and actions. These outcomes are achieved by integrating Bayesian Networks with Fuzzy and Evidence theories to estimate human error probabilities during different phases of an emergency. To test the applicability of the approach, results are compared with an analytical approach. The study demonstrates that the proposed approach is effective in assessing human error probability, which in turn improves reliability and auditability of human factors risk assessment.

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 Efforts To Reduce Downgrade Of Steel Pipes In The Production Process Using The HEART Method

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Irfan Widya Julianto ◽  
Hana Catur Wahyuni
Keyword(s):  
Production Process ◽  
Human Error ◽  
Reliability Assessment ◽  
Error Assessment ◽  
Steel Pipes ◽  
A Value ◽  
A Company ◽  
Human Error Probability ◽  
And Task

Pt X is a company that produces steel pipe of various shapes and size. In the production procces not only using machines but also using humans as operators. So in this case humans play an important role in maintaining the quality of production. The purpose of this study is to analyze the human error probability with  Human Error Assessment and Reduction Technique method (HEART). HEART is a method designed as a fast and simple human reliability assessment in quantifying the risk of human error. From this research, it was found that 3 tasks had a high HEP value which caused the decline in pipe quality during the production process, namely task 3.2, 4.1 and task 3.4. Which has the highest HEP value on task 3.2, namely setting the machine with a value of 0,7680. The cause is due to the lack of operator expertice and the center roll was not carried out when installing it, so that training is needed to increase operator expertice.

A Hybrid Methodology for Maritime Accident Analysis: The Case of Ship Collision

2019 ◽  
Author(s):  
Ludfi Pratiwi Bowo ◽  
Ramdhani Eka Prilana ◽  
Masao Furusho
Keyword(s):  
Human Error ◽  
Common Factor ◽  
Reliability Assessment ◽  
Reduction Technique ◽  
Accident Analysis ◽  
Error Assessment ◽  
Maritime Industry ◽  
Media Management ◽  
Using Data

Abstract Human error is recognized as the most common factor that causes maritime accidents. Human Error Assessment and Reduction Technique (HEART) as a Human Reliability Assessment (HRA) has been widely applied in various industries. However, in the maritime industry, machine, media, and management are also considered as factors that can strongly affect human behavior, judgment, and other human elements. These factors are particularly relevant for bridge resource management that perform the task of maintaining a proper look-out. Therefore, this study considers the effect of those factors integrated with 4M (man, machine, media, management) factors. The study was conducted with 37 collisions accident reports from 2007–2017, using data from the NTSB and TSB-Canada. There are 229 Error Producing Condition (EPC) found in this study. The classification of the EPC to the 4M conducted in this study.

Quantitative Evaluation of Human Reliability for Operating Tasks in Nuclear Power Plants

1982 ◽  
Vol 26 (9) ◽  
pp. 799-802
Author(s):  
A. Mohsen M. Metwally ◽  
Zeinab A. Sabri ◽  
S. Keith Adams ◽  
Abdo A. Husseiny
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Human Error ◽  
Human Reliability ◽  
One Dimensional ◽  
Nuclear Systems ◽  
Human Failure ◽  
Error Probabilities ◽  
Comparison Of The Results

Human reliability can be computed quantitatively provided that the operational data are put in the proper format and several information matrices are available. Both one dimensional and multidimensional trends can be easily determined by counting the number of operational errors that belong to one or more specific categories. Two examples are given for one dimensional trends in the operational tasks in 65 nuclear plants over a period of ten years. In the first example, the classification of events is made according to the cue used in error discovery. The results show that observation of unannunciated displays represents 41% of the total number of discovery cues. The second example shows that by classification of events according to the human failure mode, 50% of the total operation errors are due to omission. Recommendations and restrictions are given in detail. Assuming a constant failure rate (w.r.t. time) and an exponential human reliability model, failure rates (per hour and per demand of frequency of use) for valve mispositioning in two nuclear systems are computed. Comparison of the results obtained with other estimates of human error probabilities is made. Also, the results show that the unavailability of valves in those systems where human errors involved mispositioning is not significantly greater than that due to mechanical causes.

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