Performance human

Problems involving routine calculations are solved much faster and more reliably by computers than by humans. Nevertheless, there are tasks in which humans perform better, such as those in which the procedure is not strictly determined and problems which are not strictly algorithmic. One of these tasks is the recognition of patterns such as feces. For several decades people have been trying to develop methods which enable computers to achieve better results in these fields. One approach, artificial neural networks, which model the functionality of the brain, is explained in this section. 

Lorenzo, D. K. (1990). A Manager s Cuide to Reducing Human Errors Improving Human Performance in the Chemical Industry. Washington, D. C. Chemical Manufacturers Association. 

Qualitative assessment Determine performance requirements Evaluate performance situation Specify performance objectives Identify potential human errors Model human performance... 

Quantitative assessment Determine probabilities of human errors Identify factors and interactions affecting human performance... 

The PRA procedures guide, NUREG/ CR-23(X), partitions human reliability analysis (HRA) into four phases (Figure 4.5-1). The familiarization phase, evaluates a sequence of events to identify human actions that directly affect critical process components. From plant visits and review, this part of HRA identifies plant-specific factors that affect human performance such as good or bad procedures used in the. sequence under consideration. The familiarization phase notes items overlooked during systems evaluation. 

The analysis of human actions is complicated because a human is a responsive system like a servo. Such analysis does not lend itself to simple models as do inanimate components. Classifying human actions into the success or failure states used in logic models for plant equipment dix. s not account for the wide range of possible human actions. A generally applicable model of the parameters that affect human performance is not yet available. 

E eni An occurrence involving process, equipment, or human performance either internal or external to a system that causes system upset. 

This book has been written to show how the science of human factors can be applied at the plant level to significantly improve human performance and reduce human error, thus improving process safety. 

Chapter 2, Understanding Human Performance and Error, provides a comprehensive overview of the main approaches that have been applied to analyze, predict, and reduce human error. This chapter provides the reader with the vmderlying theories of human error that are needed to xmderstand and apply a systems approach to its reduction. 

Chapter 3, Factors Affecting Human Performance in the Chemical Industry, describes how a knowledge of "performance-influencing factors" (PIFs), can be used to identify and then eliminate error-causing conditions at the plant. 

This book brings together a wide range of tools and techniques used by human factors and human reliability specialists, which have proved to be useful in the context of human performance problems in the CPI. Although many human factors practitioners will be familiar with these methods, this book is intended to provide ready access to both simple and advanced techniques in a single source. Where possible, uses of the techniques in a CPI context are illustrated by means of case studies. 

Chapter 4 focuses on techniques which are applied to a new or existing system to optimize human performance or qualitatively predict errors. Chapter 5 shows how these teclmiques are applied to risk assessment, and also describes other techniques for the quantification of human error probabilities. Chapters 6 and 7 provide an overview of techniques for analyzing the underlying causes of incidents and accidents that have already occurred. 

The first component of the systems approach to error reduction is the optimization of human performance by designing the system to support human strengths and minimize the effects of human limitations. The hiunan factors engineering and ergonomics (HFE/E) approach described in Section 2.7 of Chapter 2 indicates some of the techniques available. Design data from the human factors literature for areas such as equipment, procedures, and the human-machine interface are available to support the designer in the optimization process. In addition the analytical techniques described in Chapter 4 (e.g., task analysis) can be used in the development of the design. 

From the 1960s onward, there was a greater interest in psychological issues, dominated by the concept of the human as a single-channel processor of information. This stimulated research into a number of areas. Studies of mental workload were concerned with the ability of humans to cope with extremely high levels of information in situations such as air traffic control. Vigilance studies, which focused on the human s role in situations with very low levels of stimulation such as radar monitoring, represented the other extreme of human performance that was considered. 

The main thrust of the HF/E approach is to provide the conditions that will optimize human performance and implicitly minimize human error. However, there is rarely any attempt to predict the nature and likelihood of specific human errors and their consequences. By contrast, the study of human error in the context of systems reliability is concerned almost exclusively with these latter issues. It is appropriate to introduce the systems reliability assessment approach to human error at this stage because, until recently, it was largely... 

The cognitive approach has had a major influence in recent years on how human error is treated in systems such as chemical process plants and nuclear power generation. In the next section we shall describe some of the key concepts that have emerged from this work, and how they apply to the analysis of error in the CPI. Discussion of the cognitive view of human performance are contained in Reason (1990), Hollnagel (1993), Kantowitz and Fujita (1990), Hollnagel and Woods (1983), and Woods and Roth (1990). 

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