Fuzzy logic-based approach for identifying the risk importance of human error

Human error plays a significant role in crane safety. To increase the accuracy and rationality of human error risk prioritization for crane operations, this study proposes a risk prioritization model for human errors in crane operations based on the cumulative prospect theory (CPT) and the improved combination weighting model of game theory (ICWGT). The ICWGT integrates the risk-factor weights obtained via subjective and objective methods. Trapezoidal fuzzy numbers are used to describe experts’ uncertainty information. Then, the CPT is applied to handle the assessment of experts’ risk attitudes in the decision process. The human error risk ranking of crane operations is obtained according to the overall prospect values calculated using the CPT. A case study of human error in overhead crane operations was conducted, and sensitivity and comparison analyses confirmed the feasibility of the proposed model. The proposed ranking mechanism for human error risk priority in crane operations is helpful for crane risk management.

The most serious problem for human error estimation is the scarcity of empirical data on human performance for the development and validation of Human Reliability Analysis (HRA) approaches. This issue is strongly evident in manufacturing systems, where the data collection and availability of a meaningful dataset to feed human reliability have severe constraints related to time-resource consuming and accuracy of the collection approach. This paper proposes a beginning taxonomy of human error consequences in order to support data collection in manufacturing systems and to identify experimental human error probability (HEP). This taxonomy is the first step of the methodology for HEP analysis and quantification and for validation of theoretical curves of Simulator for Human Error Probability Analysis (SHERPA) model. A new statistical methodology is able to quantify experimental HEPs, starting from the realistic human error consequences, and to compare them with the SHERPA theoretical human error distributions. This methodology, used for the SHERPA validation, may be useful for assessing current HEP estimation into HRA approaches.

Preparation for a sea voyage is one of the fundamental aspects of navigation. Several complexities are involved during the preparation of the ship for navigation due to the nature of maritime work. At this point, analysing human-related error is of paramount importance to ensure the safety of the ship and the crew. This paper describes the principles of a methodology, namely fuzzy-based shipboard operation human reliability analysis (SOHRA), to quantitatively perform human error assessment through procedures of preparing the ship for navigation. While the SOHRA (a marine-specific HRA approach) quantifies human error, the fuzzy logic deals with ambiguity and vagueness in the human error detection problem. The findings show that the total HEP (Human error probability) is found 1.49E-01 for preparing the ship for navigation. Consequently, the paper provides practical contributions to shore-based safety professionals, ship managers, and masters of the ship since it performs a systematic human reliability assessment and enhances safety control levels in the operational aspect.

Objective In this study, a hybrid human error assessment (HHEA) approach is proposed to determine the probability of aircraft maintenance technician (AMT) error occurring during aircraft repair. Background The human error assessment and reduction technique (HEART) is the first-generation empirical human reliability analysis (HRA) approach. Although this technique is used in several industries, AMTs’ errors have not been assessed with HEART and interactions of error-producing conditions (EPCs) have not been considered in calculating the probability of AMTs’ errors. Method HHEA is proposed to reduce the subjectivity of experts’ judgments by calculating assessed proportion of affect of each EPC on human error probability (HEP) integrating HEART and the analytic network process (ANP) method. The proposed model has been applied in the maintenance of the elevator, which is one of the most important primary control surfaces for the Cessna 172 Series aircraft type. Results The HEP values of 3 subtasks are found to be higher than the average HEP value. These subtasks have the highest probability of human error due to time pressure, poor environmental conditions, and being a repetitive task. Conclusion This study could guide other aircraft maintenance organizations using this integration of human reliability assessment approach for critical maintenance practices to enhance flight safety and minimize human error.

Manual assessment of coffee bean quality is often subject to inconsistency and evaluator bias, potentially leading to economic losses for farmers and industry stakeholders. This study proposes an objective simulation model for coffee bean quality evaluation using the Mamdani Fuzzy Logic method implemented in MATLAB Fuzzy Logic Toolbox. A quantitative descriptive approach was adopted, utilizing secondary data synthesized from validated experimental literature. The model incorporates three physical input variables: kadar air (%), color intensity (lux), and bean size (mm), with a single output variable representing the quality score. The system process includes fuzzification using trapezoidal and triangular membership functions, evaluation of 27 IF–THEN rules, aggregation through the MAX operator, and defuzzification using the Centroid method. Simulation results demonstrate that the proposed system effectively classifies coffee bean quality into three categories: Premium, Sedang, and Rendah. Under optimal conditions (11.5% moisture content, bright color, and large bean size), the defuzzified score consistently exceeds 70 (Grade 1), aligning with industry quality standards. The findings confirm that fuzzy logic provides a transparent and reliable decision-support framework capable of transforming ambiguous physical measurements into measurable quality indices for coffee standardization.

Human error risk analysis in offshore emergencies

Quantification of performance shaping factors (PSFs)’ weightings for human reliability analysis (HRA) of low power and shutdown (LPSD) operations

Human error is one of the significant factors contributing to accidents. Traditional human error probability (HEP) studies based on fuzzy number concepts are one of the contributions addressing such a problem. It is particularly useful under circumstances where the lack of data exists. However, the degree of the discriminability of such studies may be questioned when applied under circumstances where experts have adequate information and specific values can be determined in the abscissa of the membership function of linguistic terms, that is, the fuzzy data of each scenario considered are close to each other. In this article, a novel HEP assessment aimed at solving such a difficulty is proposed. Under the framework, the fuzzy data are equipped with linguistic terms and membership values. By establishing a rule base for data combination, followed by the defuzzification and HEP transformation processes, the HEP results can be acquired. The methodology is first examined using a test case consisting of three different scenarios of which the fuzzy data are close to each other. The results generated are compared with the outcomes produced from the traditional fuzzy HEP studies using the same test case. It is concluded that the methodology proposed in this study has a higher degree of the discriminability and is capable of providing more reasonable results. Furthermore, in situations where the lack of data exists, the proposed approach is also capable of providing the range of the HEP results based on different risk viewpoints arbitrarily established as illustrated using a real-world example.

Objectives. A necessity for this study was felt in the catalyst replacement process as a maintenance operation, because some fatal incidents have occurred due to human error in process industries during catalyst replacement operation. Identification and evaluation of human error is essential in predicting and reducing accidents in maintenance operation. Methods. The human error assessment and reduction technique (HEART) as a human reliability analysis was applied in this study. Because the HEART method uses expert judgment, it is possible to make a bias in the assessment. Fuzzification and aggregation opinions of a heterogeneous expert group in a trapezoidal fuzzy set were used to mitigate this deficiency. Results. Evaluation results showed that the subtask ‘Entering the reactor’ with human error probability (HEP) of 9.2E–1 and the subtask ‘Reduce temperature while reducing feed’ with HEP 1.3E–3 had the highest and lowest chance of human error, respectively. Conclusions. In order to reduce the likelihood of human error, it is necessary to take appropriate actions based on the task-specific error producing condition (EPC).

Risk analyses require proper consideration and quantification of the interaction between humans, organization, and technology in high-hazard industries. Quantitative human reliability analysis approaches require the estimation of human error probabilities (HEPs), often obtained from human performance data on different tasks in specific contexts (also known as performance shaping factors (PSFs)). Data on human errors are often collected from simulated scenarios, near-misses report systems, and experts with operational knowledge. However, these techniques usually miss the realistic context where human errors occur. The present research proposes a realistic and innovative approach for estimating HEPs using data from major accident investigation reports. The approach is based on Bayesian Networks used to model the relationship between performance shaping factors and human errors. The proposed methodology allows minimizing the expert judgment of HEPs, by using a strategy that is able to accommodate the possibility of having no information to represent some conditional dependencies within some variables. Therefore, the approach increases the transparency about the uncertainties of the human error probability estimations. The approach also allows identifying the most influential performance shaping factors, supporting assessors to recommend improvements or extra controls in risk assessments. Formal verification and validation processes are also presented.

A comparison of the quantification aspects of human reliability analysis methods in nuclear power plants

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.

Fuzzy and Neuro Fuzzy logic concepts have been used for variety of applications, where machine intelligence can be used for the system. The fuzzy logic concept has been used for a number of biomedical applications also. We have used fuzzy and neuro fuzzy approach for tackling surgery procedures of a patient suffering from stress urinary incontinence (SUI). We have further extended the application of Fuzzy and Neuro Fuzzy logic for predicting the approximate recovery time of a patient under physical therapy suffering from stroke, paraplegia, quadriplegia etc. Here, we have outlined methods for above mentioned biomedical applications based on a knowledge base created from the patients of different age groups. We have shown our simulation results with input data and possible outcomes with the Fuzzy and Neuro Fuzzy system.

Reactor safety and risk are dominated by the potential and major contribution for human error in the design, operation, control, management, regulation and maintenance of the plant, and hence to all accidents. Given the possibility of accidents and errors, now we need to determine the outcome (error) probability, or the chance of failure. Conventionally, reliability engineering is associated with the failure rate of components, or systems, or mechanisms, not of human beings in and interacting with a technological system. The probability of failure requires a prior knowledge of the total number of outcomes, which for any predictive purposes we do not know or have. Analysis of failure rates due to human error and the rate of learning allow a new determination of the dynamic human error rate in technological systems, consistent with and derived from the available world data. The basis for the analysis is the “learning hypothesis” that humans learn from experience, and consequently the accumulated experience defines the failure rate. A new “best” equation has been derived for the human error, outcome or failure rate, which allows for calculation and prediction of the probability of human error. We also provide comparisons to the empirical Weibull parameter fitting used in and by conventional reliability engineering and probabilistic safety analysis methods. These new analyses show that arbitrary Weibull fitting parameters and typical empirical hazard function techniques cannot be used to predict the dynamics of human errors and outcomes in the presence of learning. Comparisons of these new insights show agreement with human error data from the world’s commercial airlines, the two shuttle failures, and from nuclear plant operator actions and transient control behavior observed in transients in both plants and simulators. The results demonstrate that the human error probability (HEP) is dynamic, and that it may be predicted using the learning hypothesis and the minimum failure rate, and can be utilized for probabilistic risk analysis purposes.

A probabilistic model of human error assessment for autonomous cargo ships focusing on human–autonomy collaboration