Towards A Better Approach for Mastering Industrial Risks From Modeling Accidental Process to Integrating Safety Analysis Techniques Supporting the Identification of Intelligent Safety Decision

Abstract

This paper introduces a research aiming at the development of a new approach to mastering industrial risks and prevent accident scenario. Starting from modeling and analyzing accidental process to understand the causes of accidents using quantitative risk assessment and reliability, they are essential issues in modern safety to make reliable decision, and it is new approach used for risk management to identify accident scenarios that may occur at their facilities which are sources of damage. Risk assessment of safety instrumented systems is approaches designed primarily to reduce the existing risk inherent in engineering system to a level considered tolerable and maintain it over time. In this study, the reliability of quantitative risk assessment using fuzzy sets based on event tree analysis and layer of protection analysis is the model proposed to deal with inaccuracy and uncertainty of data, The model proposed to determine the severity of the scenario and determine the safety integrity level SIL using Fuzzy Sets theory. The results which have got by this model is more motivate to deal with uncertainty of which considered as complementary for logical and arithmetic computation. As the accident is a chain of failure events, each related to its (causal) event or events, the early detection and diagnosis of faults in processes is very important, we use Fault Tree Analysis to show the possible malfunctions by enumerating the suspect components and their respective failure modes. Fault diagnosis when error occurs is performed by engineers and analysts performing extensive examination of all data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, we present in this approach to use decision trees to capture the contents of fault trees and detect faults by running the telemetry data through the decision trees in real time. Decision trees are the binary trees built from data samples and can classify the objects into different classes, the decision trees can classify different fault events or normal events. Given a set of data samples, decision trees can be built and trained, and then by running the new data through the trees, classification and prediction can be made.