Human error recognition

As microprocessor-based controls displaced hardwired electronic and pneumatic controls, the impac t on plant safety has definitely been positive. When automated procedures replace manual procedures for routine operations, the probability of human errors leading to hazardous situations is lowered. The enhanced capability for presenting information to the process operators in a timely manner and in the most meaningful form increases the operator s awareness of the current conditions in the process. Process operators are expected to exercise due diligence in the supervision of the process, and timely recognition of an abnormal situation reduces the likelihood that the situation will progress to the hazardous state. Figure 8-88 depicts the layers of safety protection in a typical chemical jdant. 

Human error is always a significant factor in elemental analysis. Inadequately trained, inexperienced or simply inattentive analysts may not be able to recognize a developing problem with a potential for blank and sample contamination and to handle it correctly. Unskilled analysts may cause laboratory contamination or cross-contamination between samples due to poor laboratory technique. There is no substitute to proper training and experience for the recognition and prevention of laboratory contamination in the trace level elemental analysis. 

A very important factor in safety management and safety analysis is the recognition of the importance of the human intervention in the related activities. Human errors should be avoided by the establishment of clear interfaces between man and machine, and by the preparation of operating and emergency procedures and of maintenance rules and guidelines. Beneficial human intervention, even in extremely degraded situations, should be implemented by adequate training, procedures and simulation studies and practices. 

It is now recognized that many errors in routine interactive behaviour are not the product of some stochastic process, and that causal explanations of human error can be developed [1]. However, little is known about what factors influence an individual s ability to recognize errors. Recognition that an error has been made is a prerequisite for error recovery. The focus of this paper is on this recognition process rather than the error recovery process as a whole. 

THERP can also be used for event tree modeling. For example, an initiating event could be an emergency situation such as a leak of a hazardous chemical. Items in the event tree that could incorporate human error include recognition that a leak has occurred, identifying the nature of the leak, and using the correct emergency response equipment. 

The Safe System approach is a fundamental shift from traditional traffic safety thinking. It reframes the ways in which traffic safety is viewed and managed. Its aim is to support development of a transport system better able to accommodate inevitable human error. The recognition that humans do make, and will continue to make, errors of judgement as road users is one of the core shifts in thinking. 

An automatic peak search is actually the simplest (one-dimensional) case in the more general two- or three-dimensional image recognition problem. Image recognition is easily done by a human eye and a brain but is hard to formalize when random errors are present and, therefore, difficult to automate. Many different approaches and methods have been developed two of them are most often used in peak recognition and will be discussed here. These are the second derivative method and the profile scaling technique. 

Unfortunately, many classes of objects that humans can readily recognize are very difficult to characterize in this way. Object classes such as trees, chairs, or even handprinted characters do not have simple generic descriptions. One can characterize such classes by simplified, partial descriptions, but since these descriptions are usually incomplete, using them for object recognition will result in many errors. Even the individual parts of objects are often difficult to model many natural classes of shapes (e.g., clouds) or of surface textures (e.g., tree bark) are themselves difficult to characterize. 

A type of safety identification review that methodically analyzes the interactions between individuals and machines. It reviews the operation phase to operational phase, while considering the consequences of operator-system faults at each operating step within each phase. This analysis allows for the recognition of threats from equipment faults that may coexist with operator errors. It is considered similar to a Failure Mode and Effects Analysis (FMEA), but with increased emphasis on the steps in human procedures rather than viewing hardware exclusively. See also Failure Mode and Effects Anafysis (FMEA) Job Safety Analysis (JSA). 

It becomes clear from an examination of this list that humans appear to surpass machines in detection, pattern recognition, flexibility, inductive reasoning and judgement. On the other hand, machines appear to surpass humans in speed, precision of response and application of sustained power, repetitive performance, short term memory, deductive reasoning and multichannel performance. The human does however have one particular crucial ability that of his inbuilt error-correction and error-monitoring facility, which coupled with his great flexibility and versatility means that he can often detect then act to minimise the consequence of error. 

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