System-induced error approach

The structure of this book is based on a model of human error, its causes, and its role in accidents that is represented by Figures 1.4 and 1.5. This perspective is called the system-induced error approach. Up to now, only certain... 

The system-induced error approach can be restated in an alternative form as an accident causation model (see Figure 1.4). This shows how error-inducing conditions in the form of inadequate PIFs interact with error tendencies to... 

A CASE STUDY ILLUSTRATING THE SYSTEM-INDUCED ERROR APPROACH... 

This chapter has provided an overview of the book and has described its underlying philosophy, the system-induced error approach (abbreviated to the systems approach in subsequent chapters). The essence of the systems approach is to move away from the traditional blame and punishment approach to human error, to one which seeks to understand and remedy its underlying causes. 

As described in Chapters 1 and 2 the system-induced error approach comprises the following elements ... 

I.4. Implications of the System-Induced Error Approach for Data Collection... 

Because of the emphasis on modeling accident causation, data collection systems based on the system-induced error approach are likely to modify their data collection strategies over time. Thus, as evidence accumulates that the existing causal categories are inadequate to accoimt for the accidents and near misses that are reported, the data collection philosophy will be modified, and a new accident causation model developed. This, in turn, will be modified on the basis of subsequent evidence. 

There are many other examples of such a systems approach to reducing drug-induced illness caused by this kind of error. 

This example demonstrates that even for a deterministic system without any external loading, the response appears to be uncertain. In the real world, there are many types of unmodeled behavior/dynamics of complex physical phenomena (e.g., chaotic systems) and one possible approach is to treat them as random variables or random processes. Then, statistical moments are used to represent the overall behavior. This type of error is regarded hereafter as a modeling error. Another main source of uncertainty is due to the finite amount of information carried by the data. Due to the finite amount of the measurement, and hence the finite amount of information, identification results can be determined up to finite precision so uncertainty gets into the picture. Finally, due to the finite precision of data acquisition, measurement error is induced, including electrical noise and quantization error. 

Influenced by the mind of forward modeling problems, it is easily directed to adopt complicated model classes so as to capture various complex physical mechanisms. However, the more complicated the model class is utilized, the more uncertain parameters are normally induced unless extra mathematical constraints are imposed. In the former case, the model output may not necessarily be accurate even if the model well characterizes the physical system since the combination of the many small errors from each uncertain parameter can induce a large output error. In the latter case, it is possible that the extra constraints induce substantial errors. Therefore, it is important to use a proper model class for system identification purpose. In this chapter, the Bayesian model class selection approach is introduced and applied to select the most plausible/suitable class of mathematical models representing a static or dynamical (structural, mechanical, atmospheric,...) system (from some specified model classes) by using its response measurements. This approach has been shown to be promising in several research areas, such as artificial neural networks [164,297], structural dynamics and model updating [23], damage detection [150] and fracture mechanics [151], etc. 

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