Error definitions

A better alternative would be to use the propagation of errors definition, which takes into consideration values of both and si when calculating the MDL. This would involve generating at least five calibration curves in order to obtain an accurate measurement of si and Sm ... 

Core and supplemental water quality indicators The design should be have appropriate levels of precision and confidence (integrating monitoring objective and type of date collected) as well as methods to control and balance possible decision errors Definition of a core set of indicators for each water resource type which can be used routinely to assess WQS... 

Type of Error Definition Example from Aviation Example from Health Care Examples of Remediation... 

Type A alarm about a runtime error which has unpredictable results. The analyzer reports the alarm and continues the analysis for scenarios where the error does not occur. For contexts where the error definitely occurs, the analyzer reports a definite runtime error and stops the analysis as there is no feasible continuation. Examples are out-of-bound array accesses, or write accesses via dangling pointers. 

Lack of consistent human error definition. Human error is defined in many similar yet distinct manners, thus resulting to the lack of a consistent approach for identification and, consequently, taking of response measures. 

Sasou, K., and J. Reason. Team Errors Definition and Taxonomy. Reliability Engineering and System Safety 65, no. 1 (1999) 1-9. 

The described method can generate a first-order backward or a first-order forward difference scheme depending whether 0 = 0 or 0 = 1 is used. For 9 = 0.5, the method yields a second order accurate central difference scheme, however, other considerations such as the stability of numerical calculations should be taken into account. Stability analysis for this class of time stepping methods can only be carried out for simple cases where the coefficient matrix in Equation (2.106) is symmetric and positive-definite (i.e. self-adjoint problems Zienkiewicz and Taylor, 1994). Obviously, this will not be the case in most types of engineering flow problems. In practice, therefore, selection of appropriate values of 6 and time increment At is usually based on trial and error. Factors such as the nature of non-linearity of physical parameters and the type of elements used in the spatial discretization usually influence the selection of the values of 0 and At in a problem. 

The temperature compensator on a pH meter varies the instrument definition of a pH unit from 54.20 mV at 0°C to perhaps 66.10 mV at 60°C. This permits one to measure the pH of the sample (and reference buffer standard) at its actual temperature and thus avoid error due to dissociation equilibria and to junction potentials which have significant temperature coefficients. 

Uncertainty expresses the range of possible values that a measurement or result might reasonably be expected to have. Note that this definition of uncertainty is not the same as that for precision. The precision of an analysis, whether reported as a range or a standard deviation, is calculated from experimental data and provides an estimation of indeterminate error affecting measurements. Uncertainty accounts for all errors, both determinate and indeterminate, that might affect our result. Although we always try to correct determinate errors, the correction itself is subject to random effects or indeterminate errors. 

Significance tests, however, also are subject to type 2 errors in which the null hypothesis is falsely retained. Consider, for example, the situation shown in Figure 4.12b, where S is exactly equal to (Sa)dl. In this case the probability of a type 2 error is 50% since half of the signals arising from the sample s population fall below the detection limit. Thus, there is only a 50 50 probability that an analyte at the lUPAC detection limit will be detected. As defined, the lUPAC definition for the detection limit only indicates the smallest signal for which we can say, at a significance level of a, that an analyte is present in the sample. Failing to detect the analyte, however, does not imply that it is not present. 

Normal distribution curves showing the definition of detection limit and limit of identification (LOI). The probability of a type 1 error is indicated by the dark shading, and the probability of a type 2 error is indicated by light shading. 

Design of experiments. When conclusions are to be drawn or decisions made on the basis of experimental evidence, statistical techniques are most useful when experimental data are subject to errors. The design of experiments may then often be carried out in such a fashion as to avoid some of the sources of experimental error and make the necessary allowances for that portion which is unavoidable. Second, the results can be presented in terms of probability statements which express the reliabihty of the results. Third, a statistical approach frequently forces a more thorough evaluation of the experimental aims and leads to a more definitive experiment than would otherwise have been performed. 

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. 

Definitive e.stimate (project-control e.stimate). Based on considerable data prior to preparation of completed drawings and specifications probable error within 10 percent. 

At X-ray fluorescence analysis (XRF) of samples of the limited weight is perspective to prepare for specimens as polymeric films on a basis of methylcellulose [1]. By the example of definition of heavy metals in film specimens have studied dependence of intensity of X-ray radiation from their chemical compound, surface density (P ) and the size (D) particles of the powder introduced to polymer. Have theoretically established, that the basic source of an error of results XRF is dependence of intensity (F) analytical lines of determined elements from a specimen. Thus the best account of variations P provides a method of the internal standard at change P from 2 up to 6 mg/sm the coefficient of variation describing an error of definition Mo, Zn, Cu, Co, Fe and Mn in a method of the direct external standard, reaches 40 %, and at use of a method of the internal standard (an element of comparison Ga) value does not exceed 2,2 %. Experiment within the limits of a casual error (V changes from 2,9 up to 7,4 %) has confirmed theoretical conclusions. 

For exposure of reasons of observable discrepancy of results of the analysis simulated experiment with application synthetic reference samples of aerosols [1]. The models have demonstrated absence of significant systematic errors in results XRF. While results AAA and FMA depend on sort of chemical combination of an elements, method of an ashing of a material and mass of silicic acid remaining after an ashing of samples. The investigations performed have shown that silicic acid adsorbs up to 40 % (rel.) ions of metals. The coefficient of a variation V, describing effect of the indicated factors on results of the analysis, varies %) for Mn and Fe from 5 up to 20, for Cu - from 10 up to 40, for Pb - from 10 up to 70, for Co the ambassador of a dry ashing of samples - exceeds 50. At definition Cr by a method AAA the value V reaches 70 %, if element presences an atmosphere in the form of Cr O. At photometric definition Cr (VI) the value V is equal 40%, when the element is present at aerosols in the form of chromates of heavy metals. 

To be able to systematically identify opportunities for reducing human error, it is useful to ask the question, What is human error One definition is that human error is an inappropriate or undesirable human decision or behavior that reduces, or has the potential for reducing safety or system performance (Rasmusssen 1979). There is a tendency to view errors as operator errors. However, the error may result from inadequate management, design, or maintenance of the system. This broader view which encompasses the whole system can help provide opportunities for instituting measures to reduce the likelihood of errors. 

Each binary fork is attached to a branch of the preceding fork and is conditioned by the success or failure represented by that branch. Thus, evei7 fork, represents conditional probability. Each limb of the HRA event tree is described or labeled, in shorthand. Capital letters (A) represent I ailure lower case letters (a) represent success. The same convention applies to Greek letters, which represent non-human error events, such as equipment failures. The letters S and F are exceptions to this rule in that they represent system success and failure respectively, in practice, the limbs may be labeled with a short description of the error lo eliminate the need for a legend. The labeling format is unimportant the critical task in developing HRA event trees is the definition of the events themselves and their translation to the trees. 

The time difference (delay) between the measured quantity and the measurement result is called the inertial error. A definition- is the error due to iner tia (mechanical, thermal, etc.) of the parts of a measuring instrument. In ventilation equipment the critical component in the measuring chain, from the dynamic point of view, is often the sensor or the measuring transducer (probe). 

Improvement in business performance is essential for growth and profit, but the ISO/TS 16949 requirements are not concerned with your growth and profits they are concerned with product quality, and one definition of product quality that signals improvement potential is freedom from defects . Achieving quality become a quest to eliminate defects and in so doing reduces variation in the operational processes, but even when there are no defectives, there will still be variation. One might well question the need to reduce variation when there are no defectives but by reducing variation you will have fewer breakdowns, fewer errors, less space allocated to inventory, less waste, etc. in fact fewer problems and increased profit as a result. 

Returning to the standard, this clause also only addresses the correction and prevention of nonconformities, i.e. departures from the specified requirements. It does not address the correction of defects, of inconsistencies, of errors, or in fact any deviations from your internal specifications or requirements. As explained in Part 2 Chapter 13, if we apply the definition of nonconformity literally, a departure from a requirement that is not included in the Specified Requirements is not a nonconformity and hence the standard is not requiring corrective action for such deviations. Clearly this was not the intention of the requirement because preventing the recurrence of any problem is a sensible course of action to take, providing it is economical. Economics is, however, the crux of the matter. If you include every requirement in the Specified Requirements , you not only overcomplicate the nonconformity controls but the corrective and preventive action controls as well. 

The corrective action requirements fail to stipulate when corrective action should be taken except to say that they shall be to a degree appropriate to the risks encountered. There is no compulsion for the supplier to correct nonconformities before repeat production or shipment of subsequent product. However, immediate correction is not always practical. You should base the timing of your corrective action on the severity of the nonconformities. All nonconformities are costly to the business, but correction also adds to the cost and should be matched to the benefits it will accrue (see later under Risks). Any action taken to eliminate a nonconformity before the customer receives the product or service could be considered a preventive action. By this definition, final inspection is a preventive action because it should prevent the supply of nonconforming product to the customer. However, an error becomes a nonconformity when detected at any acceptance stage in the process, as indicated in clause 4.12 of the standard. Therefore an action taken to eliminate a potential nonconformity prior to an acceptance stage is a preventive action. This rules out any inspection stages as being preventive action measures - they are detection measures only. 

A single, all-embracing definition of human error is difficult to achieve. For the engineer, the worker in a system such as a chemical process plant may be... 

The analysis of accidents and disasters in real systems makes it clear that it is not sufficient to consider error and its effects purely from the perspective of individual human failures. Major accidents are almost always the result of multiple errors or combinations of single errors with preexisting vulnerable conditions (Wagenaar et al., 1990). Another perspective from which to define errors is in terms of when in the system life cycle they occur. In the following discussion of the definitions of human error, the initial focus will be from the engineering and the accident analysis perspective. More detailed consideration of the definitions of error will be deferred to later sections in this chapter where the various error models will be described in detail (see Sections 5 and 6). 

From a reliability engineering perspective, error can be defined by analogy with hardware reliability as "The likelihood that the human fails to provide a required system function when called upon to provide that fimction, within a required time period" (Meister, 1966). This definition does not contain any references to why the error occurred, but instead focuses on the consequences of the error for the system (loss or unavailability of a required function). The disadvantage of such a definition is that it fails to consider the wide range of other actions that the human might make, which may have other safety implications for the system, as well as not achieving the required function. 

Although the above descriptions are, strictly speaking, classifications rather than definitions of error, they share the same characteristics as the first definition in that they describe what happened rather than why it happened. They are therefore much more easily related to the observable consequences of an error than to its causes. 

Violation Error/Failure A violation error occurs when an intended action is made which deliberately ignores known operational rules, restrictions, or procedures. However, this definition excludes actions that are deliberately intended to harm the system, which come within the category of sabotage. 

In the above definitions, the term "error" is used for the error event itself, and "failure" for the consequences of the error event. 

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