Computer methods risk analysis

FIGURE 7 The process failure risk analysis (PFRA) tool is an analytical and computational tool using rule bases for evaluating process risks. It is an ideal method and tool for reducing costly failures. (For more about this software tool, see http //www.cimwareukandusa.com. 

The overall concept of all of the following tools is that of risk analysis or risk assessment. Risk analysis helps to decide whether an aspect is GMP-critical or not. The risk analysis can be performed in a formal or more informal way. Following are two popular and import types of risk analysis. Another method, the fault tree analysis (FTA), has recently been used in the area of computer validation. This method is not described here, as it is a complex form of risk analysis. 

Risk limits have always to be regarded in relation with the procedure used for risk assessment. The degree of conservativeness, of detail etc. in determining risks influence the calculated result (vid. Chaps. 9 and 10). In order to ensure equitable treatment the application of risk limits requires a largely unified, convention-based procedure of analysis. This is achieved, for example in the Netherlands, by using the computer program PH AST [31]. It contains algorithms for the methods of analysis and default values for many of the required input parameters. 

Zio, E. 2009.Computational Method for Reliability and Risk Analysis, Series in Quality, Reliability and Engineering Statistics. Vol. 14, World Scientific, Singapore. 

Performing estimation and risk analysis in the presence of uncertainty requires a method that reproduces the random nature of certain events (such as failures in the context of reliability theory). A Monte-Carlo simulation addresses this issue by running a model many times and picking values from a predefined probability distribution at each run (Mun 2006). This process allows the generation of output distributions for the variables of interest, from which several statistical measures (such as mean, variance, skewness) can be computed and analyzed. 

Gonzalez L (2002) A new method for ordering binary states probabilities in reliability and risk analysis. Lect Notes Comput Sci 2329 137-146... 

In the last twenty years, various non-deterministic methods have been developed to deal with optimum design under environmental uncertainties. These methods can be classified into two main branches, namely reliability-based methods and robust-based methods. The reliability methods, based on the known probabiUty distribution of the random parameters, estimate the probability distribution of the system s response, and are predominantly used for risk analysis by computing the probability of system failure. However, variation is not minimized in reliability approaches (Siddall, 1984) because they concentrate on rare events at the tail of the probability distribution (Doltsinis and Kang, 2004). The robust design methods are commonly based on multiobjective minimization problems. The are commonly indicated as Multiple Objective Robust Optimization (MORO) and find a set of optimal solutions that optimise a performance index in terms of mean value and, at the same time, minimize its resulting dispersion due to input parameters uncertainty. The final solution is less sensitive to the parameters variation but eventually maintains feasibility with regards probabilistic constraints. This is achieved by the optimization of the design vector in order to make the performance minimally sensitive to the various causes of variation. 

Malinowski, J., 2013. A method of computing the interstate transition intensities for multi-state series-parallel systems. Safety, Reliability and Risk Analysis Beyond the Horizon. Proceedings of the European Safety and Reliability Conference, ESREL2013 l i- l 9. 

Semi-quantitative methods use quahtative as well as quantitative evaluation procedures. Methods of indexing and ranging of risks also belong to this method. From the practical point of view, it is difficult to appraise the value of indexing methods for risk analysis purposes. Unlike quahtative methods it gives a quantitative assessment of a risk level, but differs in straightforwardness demanding for computer recourses. It also uses a small number of parameters to assess the efficacy of the FEP measurements. 

Including risk analysis steps in the planning phase of the maintenance of equipment important for safety is considered to be a good practice. A standardized method may be used, addressing all potential hazards (nuclear safety, industrial safety and radiation protection). The method is based on tools that are user friendly and transparent in application (work formulas, simple to use interactive computer sequences). For an overall risk picture at plant level, a PC based risk monitor programme may be used. The information from this planning phase is then used to verify that proper precautions are being identified and implemented. 

Zio E (2009a) Computational methods for reliability and risk analysis. World Scientific Publishing Zio E (2009b) Reliability engineering old problems and new challenges. Reliab Eng Syst Saf 94(2) 125-141... 

Adhikari S (2011) A reduced spectral function approach for the stochastic finite element analysis. Comput Methods Appl Mech Eng 200 1804-1821 Au SK, Beck JL (2001) Estimation of small failure probabilities in high dimensions by subset simulation. Probab Eng Mech 16 263-277 Au SK, Beck JL (2003) Subset simulatimi and its application to seismic risk based on dynamic analysis. J Eng Mech (ASCE) 129 901-917... 

Iman, R.L. and Conover, W.J. (1980). Small sample sensitivity analysis techniques for computer models, with an application to risk assessment. Communications in Statistics A—Theory and Methods, 9, 1749-1842. 

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