Common Performance Conditions Research Articles

Human reliability analysis method on armored vehicle system considering error correction

Human reliability analysis (HRA) is an expansion of man-machine engineering. It is also a new multidisciplinary based on behavioral science, cognitive science, information processing, system analysis and probability statistics in order to analyze, predict, reduce and prevent human errors. Firstly, the quantitative analysis model of HRA is proposed based on Markov process theory by using human error probability (HEP) and error correction cycle (ECC) as parameters. And human reliability evaluation criterion is built. Then, the HRA process considering error correction is proposed based on cognitive reliability and error analysis method (CREAM). Finally, according to the characteristics of armored vehicle system, common performance condition (CPC) in CREAM is improved. A reliability impact index is characterized by the overall contexts of tasks. Human reliability evaluation criterion of armored vehicle system is formulated. And the result of HRA is obtained based on the method presented in this paper. In addition, the relative weights are estimated by combining scale of 10/10—18/2 and analytical hierarchy process (AHP), and the triangular fuzzy number considering confidence factor and optimism index is adopted in order to reduce the subjectivity. The analysis results show that the method presented in this paper is reasonable and feasible. Meantime, the method can provide guidance for human reliability analysis of other weapon systems.

A Bayesian Network to Ease Knowledge Acquisition of Causal Dependence in CREAM: Application of Recursive Noisy‐OR Gates

Cognitive Reliability and Error Analysis Method (CREAM) is a common Human Reliability Analysis (HRA) method of second generation. In this paper, to improve the capabilities of CREAM, we propose a probabilistic method based on Bayesian Network (BN) to determine control mode and quantify Human Error Probability (HEP). The BN development process is described in a four‐phase methodology including (i) definition of the nodes and their states; (ii) building the graphical structure; (iii) quantification of BN through assessment of the Conditional Probability Tables (CPT) values and (iv) model validation. Intractability of knowledge acquisition of large CPTs is the most significant limitation of existing BN model of CREAM. So, the main contribution of this paper lies in its application of Recursive Noisy‐OR (RN‐OR) gate to treat large CPTs assessment and ease knowledge acquisition. RN‐OR allows combination of dependent Common Performance Conditions (CPCs). Finally, a quantitative HEP analysis is applied to enable more precise estimation of HEP through a probabilistic approach. Copyright © 2016 John Wiley & Sons, Ltd.

Quantifying the Severity of Marine Collision Accidents Caused by Human Factors

The severity of an accident is evaluated based on the degree of damage, the loss of human life, environmental impact, etc. Since 80% or more of marine accidents are caused by human factors, it is necessary to quantify the severity of an accident considering human factors, and it is important that the result of the quantification is made use of in accident investigation and accident prevention measures.In this study, authors examined the severity of ship collisions with numerical value using CREAM (Cognitive Reliability and Error Analysis Method) which is a human reliability analysis method.The main conclusions obtained by this study are as follows:1)CPCs (Common Performance Conditions) in CREAM were modified especially for the marine field.2)Applied modified CPSs to marine collision accident analysis.3)In consideration of the frequency of occurrence and the importance of CPCs, authors quantified the severity of marine collision accident.4)Comparing accidents designated as serious by the accident investigation standard and those designated as serious by means of numerical values derived from the importance of human factors, the effectiveness of quantifying the marine accident severity with numerical value was confirmed.

Quantitative human reliability analysis for crew during shipping operation

A simplified bi-variable human error probability calculation method is developed by incorporating two common performance condition (CPC) factors, which are modified from factors employed in cognitive reliability and error analysis method (CREAM) to take into account the characteristics of shipping operations. After the influencing factors are identified, Markov method is used to calculate the values of human reliability. The proposed method does not rely on the involvement of experts in the field of human factor nor depend on historical accidents or human error statistics. It is applied to the case of the crew on board of an ocean going dry bulk carrier. The caculated results agree with the actual case, which verifies the validity of the model.

CREAM-Based Human Reliability Analysis for Space Launch Day

According to the characteristics of space launch day, the common performance conditions (CPC) were improved. After the CPC were weighted, the value of the performance influence index was presented. And based on the modified CREAM method, a human reliability assessment was given. Consequently, the outcomes of this work can provide the human error probability and data base for safety assessment of space launch site.

A compound methodology to assess the impact of human and organizational factors impact on the risk level of hazardous industrial plants

This paper presents a compound methodology devised to relate Human and Organizational Factors (HOFs) to operators’ response time in critical operations within hazardous industrial plants. The methodology has been based on a sensitivity analysis of the nine “families” of the Common Performance Conditions (CPCs), as defined in the CREAM technique, in order to verify and rank their influence on the operators’ response time. To prove the methodology, a series of pilot experiments have been designed and performed so that human response is evaluated in a Virtual Environment (VE) reproducing the control room and a specific plant section. This environment enables the analyst controlling the simulation to perform the sensitivity analysis acting through a supervisory station and manipulating the control functions in order to vary each CPC rate around its nominal value. Experiments were run with the variation of one CPC at a time aiming at the detection and containment operation of a gas leakage in a pressure-reduction NG terminal. The whole case study has been run within the framework of the VIRTHUALIS EU project.

Screening, sensitivity, and uncertainty for the CREAM method of Human Reliability Analysis

This paper reports a sensitivity analysis of the Cognitive Reliability and Error Analysis Method for Human Reliability Analysis. We consider three different aspects: the difference between the outputs of the Basic and Extended methods, on the same HRA scenario; the variability in outputs through the choices made for common performance conditions (CPCs); and the variability in outputs through the assignment of choices for cognitive function failures (CFFs). We discuss the problem of interpreting categories when applying the method, compare its quantitative structure to that of first generation methods and discuss also how dependence is modelled with the approach. We show that the control mode intervals used in the Basic method are too narrow to be consistent with the Extended method. This motivates a new screening method that gives improved accuracy with respect to the Basic method, in the sense that (on average) halves the uncertainty associated with the Basic method. We make some observations on the design of a screening method that are generally applicable in Risk Analysis. Finally, we propose a new method of combining CPC weights with nominal probabilities so that the calculated probabilities are always in range (i.e. between 0 and 1), while satisfying sensible properties that are consistent with the overall CREAM method.

Pouring CREAM into natural gas: The introduction of Common Performance Conditions into the safety management of gas networks

Learning from operational experience is a process that has arisen as a result of the need in industry to manage the increasing complexity of technical systems. While dependability was initially dominant, limitations in its ability to account for human variability have gradually become apparent. The use of the human and organisational factors paradigm emerged in an attempt to overcome these limitations, and learning from operational experience has evolved out of this. This evolution led to a shift of interest towards the influence of the work environment on human performance. The CREAM method, structured by the Common Performance Conditions (CPCs), is a major example of this trend. This paper discusses the design and deployment of an accident analysis grid developed in 2008 by a major French gas distribution company. This grid was a response to the company’s decision to revitalise the learning from operational experience process through the use of human and organisational factors.

Quantitative Evaluation of Human-Reliability Based on Fuzzy-Clonal Selection

Human reliability analysis (HRA) is necessary for system safety assessment as well as equipment reliability analysis. The cognitive reliability and error analysis method (CREAM) as a representative HRA method provides nine common performance conditions (CPCs) to represent the contextual conditions under which a given action is performed. With a scarcity of empirical data, a high uncertainty in analyzing results is produced by subjective judgment. To obtain more objective and effective HRA results, this paper presents an optimized quantification method to evaluate the human error probability (HEP) according to CREAM, and its linguistic variables. The starting point for the quantification is an introduced fuzzy version of CREAM. However, many fuzzy rules are redundant, and occupy extensive computing time. The clonal selection algorithm combined with the fuzzy model is proposed to optimize the rule set. The evaluation method using the fuzzy-clonal selection algorithm is aimed to support possible applications for prospective and retrospective studies in the domain of safety assessment of power systems. The simulations are carried out on four presumed contexts, and a practical power system. The conclusions illuminate the feasibility of the proposed quantitative method.

Human Reliability Analysis by Fuzzy “CREAM”

This article proposes a systematic procedure for computing probabilities of operator action failure in the cognitive reliability and error analysis method (CREAM). The starting point for the quantification is a previously introduced fuzzy version of the CREAM paradigm that is here further extended to account for: (1) the ambiguity in the qualification of the conditions under which the action is performed (common performance conditions, CPCs) and (2) the fact that the effects of such conditions on human performance reliability may not all be equal.

On cognitive reliability of hospital organisational environment and work performance

Hospital organisational environment has been required to change into knowledge or intelligent organisation and each profession is able to take strategic action in coping with changing environment. Accuracy of human action is not merely acquired by avoiding erroneous behaviours, rather it is ascertained by evaluating the relationship between cognitive reliability and action. The focus of this paper is to identify the relationship between cognitive reliability and action. The incidence rate and pattern, and also perceptual differences on working conditions were analysed by applying the questionnaires of the Common Performance Conditions (CPC), the Team Member Exchange (TMX) and some other questionnaires newly prepared. The results suggested that cognitive reliability on working environments played critical roles in determining efficiency and effectiveness of organisational performance. The importance to improve organisational performance is to keep perceptual level of organisational environment higher and to improve team coherence.

Quantitative developments in the cognitive reliability and error analysis method (CREAM) for the assessment of human performance

The current ‘second generation’ approaches in human reliability analysis focus their attention on the contextual conditions under which a given action is performed rather than on the notion of inherent human error probabilities, as was done in the earlier ‘first generation’ techniques. Among the ‘second generation’ methods, this paper considers the Cognitive Reliability and Error Analysis Method (CREAM) and proposes some developments with respect to a systematic procedure for computing probabilities of action failure. The starting point for the quantification is a previously introduced fuzzy version of the CREAM paradigm which is here further extended to include uncertainty on the qualification of the conditions under which the action is performed and to account for the fact that the effects of the common performance conditions (CPCs) on performance reliability may not all be equal. By the proposed approach, the probability of action failure is estimated by rating the performance conditions in terms of their effect on the action.

Looking for errors of omission and commission or The Hunting of the Snark revisited

Since the early 1990s, considerable effort has been spent to understand what is meant by an “error of commission” (EOC), to complement the traditional notion of an “error of omission” (EOO). This paper argues that the EOO–EOC dyad, as an artefact of the PSA event tree, is insufficient for human reliability analysis (HRA) for several reasons: (1) EOO–EOC fail to distinguish between manifestation and cause; (2) EOO–EOC refer to classes of incorrect actions rather than to specific instances; (3) there is no unique way of classifying an event using EOO–EOC; (4) the set of error modes that cannot reasonably be classified as EOO is too diverse to fit into any single category of its own. Since the use of EOO–EOC leads to serious problems for HRA, an alternative is required. This can be found in the concept of error modes, which has a long history in risk analysis. A specific system for error mode prediction was tested in a simulator experiment. The analysis of the results showed that error modes could be qualitatively predicted with sufficient accuracy (68% correct) to propose this method as a way to determine how operator actions can fail in PSA-cum-HRA. Although this still leaves the thorny issue of quantification, a consistent prediction of error modes provides a better starting point for determining probabilities than the EOO–EOC dyad. It also opens a possibility for quantification methods where the influence of the common performance conditions is prior to and more important than individual failure rates.