System safety statistics

Kleijnen JPC, Helton JC (1999) Statistical analyses of scatterplots to identify important factors in large-scale simulations. 1 Review and comparison of techniques. Reliability Engineering System Safety, 65(2) 147-185. 

Cheng Liangping.2007. Development analysis on information management system of casualty accident statistics. Industrial Safety and Environmental Protec-tion (3) 56-57. 

Beck, J. L. Statistical system identification of structures. In Structural Safety and Reliability, ASCE, New York, NY (1990), pp. 1395-1402. 

While MORT is based on the fault tree method of system safety analysis, its logic diagram does not require statistical entries and computations for event probabilities. MORT is presented as an incident investigation methodology and as a basis for safety program evaluation. 

Guba A, Makai M, Pal L. Statistical aspects of best estimation method-1. Reliability Engineering System Safety 2003 80 217-232. 

Lundteigen, M.A. Rausand, M. 2009. Architectural constraints in lEC 61508 Do they have the intended effect Reliability Engineering and System Safety 94 520-525 Rausand, M. Hoyland, A. (2 ed.) 2004. System reliability theory models, statistical methods, and applications. New York Wiley... 

Through the use of basic probability theory and statistical analysis, the system safety function can actually assign expected values to certain hazards and/or failures to determine the likelihood of their occurrence. The availability of such quantifiable information further enhances the management decisionmaking process and justifies the existence of the system safety effort within the organization. 

This chapter presents the fundamental principles of probabiUty theory and briefly examines the use of statistical analysis in the practice of system safety. The information discussed here should provide the reader with a very basic understanding of these concepts, which, by some accounts, is essential to the overall understanding of the system safety discipline. It should be noted that it is not within the scope of this Basic Guide to System Safety to provide aU there is to know regarding probability theory and statistical analysis. However, a certain level of understanding is essential and will therefore be discussed here. 

Therefore, Part I of this text focused primarily on the development of system safety, its military connections, the importance of including system safety requirements in contract acquisitions, the criticality of obtaining management commitment in support of the system safety effort, the process of risk analysis and assessment, probability theory and statistical analysis as they relate to system safety, and— perhaps of most value— how the fundamental principles of system safety are closely related to those of occupational safety and health management. 

Therefore, when using the severity and probability techniques simultaneously, hazards can be examined, qualified, addressed, and resolved based upon the hazardous severity of a potential outcome and the likelihood that such an outcome will occur. For example, while an aircraft collision in midair would unarguably be classified as a Category I mishap (catastrophic), the hazard probability would fall into the Level D (remote) classification based upon statistical history of midair collision occurrence. The system safety effort in this case would require specific, but relatively minimal... 

A detailed understanding of all statistical terms and the formulas that are associated with their use is not an essential prerequisite to the practice of basic system safety analysis. A familiarization with their meaning is more than adequate for this purpose. The primary difference between statistics and probability is that probability attempts to predict the occurrence of future events, whereas statistics is used to develop models... 

Kleyner, A. Sandborn, P. 2004. A warranty forecasting model based on piecewise statistical distribution and stochastic simulation. Reliability Engineering and System Safety. 88(3) 207-214. 

Although there are a large number of probability/statistical distributions in the published literature, this section presents just five such distributions considered useful for performing oil and gas industry system safety and reliability-related studies [13-15]. 

MCMIS data populates the Analysis Information (A I) Online statistics and Safety Measurement System (SMS). The SMS uses MCMIS data to score motor carriers and drivers rmder the Comprehensive Safety Evaluation (CSA) enforcement program. If information is not noted on the inspection report, it will not be included in either the driver or carrier s SMS score and ranking among peers. For example, if a driver is given a traffic citation and is convicted, but the traffic violation is not included in the roadside inspection report, it will not be fed into the system via MCMIS. And the violation will not appear as an Unsafe Driving BASIC (Behavior Analysis Safety Improvement Category) attributed to the driver and carrier, although it will appear on the driver s motor vehicle report (MVR). 

Mishaps are assumed by many to be stochastic events, that is, random, haphazard, unpredictable. However, a mishap is more than a random unplanned event with an unpredictable free will. Mishaps are not events without apparent reason they are the result of actuated hazards. Hazards are predictable and controllable and they occur randomly based on their statistical predilection, which is typically controlled by a failure or error rate. Thinking of a mishap as a chance event without justification gives one the sense that mishaps involve an element of destiny and futility. System safety, on the other hand, is built upon the premise that mishaps are not just chance events instead they are seen as deterministic, predictable, and controllable events (in the disguise of hazards). 

Another way of interpreting absolute risk estimates is through the use of benchmarks or goals. Consider a company that operates 50 chemical process facilities. It is determined (through other, purely qualitative means) that Plant A has exhibited acceptable safety performance over the years. A QRA is performed on Plant A, and the absolute estimates are established as calibration points, or benchmarks, for the rest of the firm s facilities. Over the years, QRAs are performed on other facilities to aid in making decisions about safety maintenance and improvement. As these studies are completed, the results are carefully scrutinized against the benchmark facility. The frequency/consequence estimates are not the only results compared—the lists of major risk contributors, the statistical risk importance of safety systems, and other types of QRA results are also compared. As more and more facility results are accumulated, resources are allocated to any plant areas that are out of line with respect to the benchmark facility. 

The Seismic Safety Margins Research Program developed a computer code called SMACS (Seismic Methodology Analysis Chain with Statistics) for calculating the seismic responses of structures, systems, and components. This code links the seismic input as ensembles of acceleration time histories with the calculations of the soil-structure interactions, the responses of major structures, and the responses of subsystems. Since uses a multi-support approach to perform the time-history response calculations for piping subsystems, the correlations between component responses can be handled explicitly. SMACS is an example of the codes that are available for calculating seismic response for PSA purposes. 

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