Failure identifying factors

However, failure to disprove the null hypothesis does not mean we can reject the alternative hypothesis and accept the null hypothesis. This is a subtle but extremely important point in hypothesis testing, especially when hypothesis testing is used to identify factors in research and development projects (see Section 1.2 and Table 1.1). 

Flowers, S. (1997), Information Systems Failure Identifying the Critical FaUuie Factors, Failure and Lessons Learned in Irformation Technology Management, Vol. 1, pp. 19—29. 

There have been few studies on the lifetime of dielectric elastomer transducers and fewer still that consider lifetime of dielectric elastomer generators. Plante and Dubowsky [32] smdied failures in acrylic materials and identified factors to predict performance limitations. Kornbluh et al. [33] did report some generator lifetime results, which will be highlighted here. The requirement for long lifetime can have... 

Assigning human error as the cause of medical accident allows the health care culture to reinforce an illusion of restored safety when the individual in error is removed. This approach denies the existence of system failures, identifies a scapegoat, and prevents learning. It truncates the ability to predict and prevent future adverse events. Later, after the human cause of an accident is removed, another human being will step into place. The same conditions and factors can be reassembled, and the stage is set for the medical accident to recur. 

Once the failure modes are identified, factors that influence their generation should be determined. For example, in the requirements definition and analysis phase, a failure mode such as user is unaware of a requirement is influenced by the user s level of knowledge in the task domain. 

STEP 5 Identify factors that make these failures more likely... 

The concepts introduced in this entry for seismic design of MSWs have been related to margins of safety using classical notions of factor of safety. In this approach, factor of safety is simply the ratio of resistance capacity to load. In North American practice, the factors of safety against the modes of failure identified in Fig. 2 are reduced to values that are typically 75 % of static values or as low as 1.1. The exception is the factor of safety for overstressing (rupture) for geosynthetic reinforcement products which is taken as unity for seismic design. 

The models you use to portray failures that lead to accidents, and the models you use to propagate their effects, are attempts to approximate reality. Models of accident sequences (although mathematically rigorous) cannot be demonstrated to be exact because you can never precisely identify all of the factors that contribute to an accident of interest. Likewise, most consequence models are at best correlations derived from limited experimental evidence. Even if the models are validated through field experiments for some specific situations, you can never validate them for all possibilities, and the question of model appropriateness will always exist. 

This requirement is similar to that in clause 4.14.3 under Preventive action since the data collected for preventive action serves a similar purpose. In one case an analysis of company-level data serves to identify overall trends and predict potential failures that will affect achievement of the goals. In the preventive action case, the data serves to identify local and overall trends and predict potential failures that will affect achievement of specified requirements for the product, process, and quality system. It would be sensible to develop a data collection and analysis system that serves all levels in the organization, with criteria at each level for reporting data upwards as necessary. You should not treat this requirement separately from that for preventive action since the same data should be used. However, the explanation given in clause 4.1.5 of Operational performance does include some factors that may not be addressed in your preventive action procedures. 

HFAM has 20 groups of factors instead of the 10 general failure types of the TRIPOD approach. The reason for this is that all of the 10 TRIPOD GFTs would be applied in all situations, even though the actual questions that make up the factors may vary. In the case of HFAM, it would be rare to apply all of the factors unless an entire plant was being evaluated. HFAM uses a screening process to first identify the major areas vulnerable to human error. The generic factors and appropriate job specific factors are then applied to these areas. For example, control room questions would not be applied to maintenance jobs. 

The CADET technique can be applied both proactively and retrospectively. In its proactive mode, it can be used to identify potenrial cognitive errors, which can then be factored into CPQRA analyzes to help generate failure scenarios arising from mistakes as well as slips. As discussed in Chapter... 

Particle count tests are important to anticipating potential system or machine problems. This is especially true in hydraulic systems. The particle count analysis made a part of a normal lube oil analysis is quite different from wear particle analysis. In this test, high particle counts indicate that machinery may be wearing abnormally or that failures may occur because of temporarily or permanently blocked orifices. No attempt is made to determine the wear patterns, size and other factors that would identify the failure mode within the machine. 

One of the key aspects in developing a method for regulatory analysis is method ruggedness. The more rugged a method, the less susceptible it is to failure or to excessive variations due to differences in equipment, analyst technique, and other differences that are typically present among laboratories. Several factors contribute to poor method ruggedness insufficient testing by the developer, excessive method complexity, and a failure of the developer to identify and communicate critical points. 

Identify exacerbating or precipitating factors that may worsen BE s heart failure. 

Regardless of the underlying etiology, all seizures involve a sudden electrical disturbance of the cerebral cortex. A population of neurons fires rapidly and repetitively for seconds to minutes. Cortical electrical discharges become excessively rapid, rhythmic, and synchronous. This phenomenon is presumably related to an excess of excitatory neurotransmitter action, a failure of inhibitory neurotransmitter action, or a combination of the two. In the individual patient, however, it is usually impossible to identify which neurochemical factors are responsible. 

In both children and adults with ALL, clinical trials have identified several risk factors that correlate with outcome (Table 92-5). Prognostic features include age, WBC count, cytogenetic abnormalities, ploidy, leukemic cell immunophe-notype, and degree of initial response to therapy.7 When these factors are combined, they predict groups of patients with varying degrees of risk for treatment failure. 

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