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The goal of this research was to integrate a previously validated and reliable safety model, called Continuous Hazard Tracking and Failure Prediction Methodology (CHTFPM), into a software application. This led to the development of a safety management information system (PSMIS). This means that the theory or principles of the CHTFPM were incorporated in a software package; hence, the PSMIS is referred to as CHTFPM management information system (CHTFPM MIS). The purpose of the PSMIS is to reduce the time and manpower required to perform predictive studies as well as to facilitate the handling of enormous quantities of information in this type of studies. The CHTFPM theory encompasses the philosophy of looking at the concept of safety engineering from a new perspective: from a proactive, than a reactive, viewpoint. That is, corrective measures are taken before a problem instead of after it happened. That is why the CHTFPM is a predictive safety because it foresees or anticipates accidents, system failures and unacceptable risks; therefore, corrective action can be taken in order to prevent all these unwanted issues. Consequently, safety and reliability of systems or processes can be further improved by taking proactive and timely corrective actions. Quintana, Rolando Kennedy Space Center NAG10-331
11 . 2 Study objectives 147 11 . 3 Approach to analysis 147 11. 4 Presentation and discussion of results 151 11 . 5 Conclusions 165 12 Accident management and failure analysis G. C. Meggitt 170 12. 1 Introduction 170 12. 2 Nuclear safety 170 12. 3 The accident 171 12. 4 The accident response 171 12. 5 The automatic response 171 12. 6 The tailored response 173 12. 7 The emergency plan 181 13 Decision support systems and emergency management M. Grauer 182 13. 1 Introduction 182 13. 2 The problem 183 13. 3 The multiple-criteria approach 184 3 13. 4 OveNiew of the 1-decision support software 186 13. 5 A case study from chemical industry 189 13. 6 Conclusions 195 References 196 14 Safety integrity management using expert systems Dr P. Andow 198 14. 1 Introduction 198 14. 2 Safety and risk analysis 198 14. 3 The effects of applying safety and risk analysis 199 14. 4 Safety integrity management 201 14. 5 Knowledge-base contents 204 14. 6 Summary of system functions 204 14. 7 Discussion 205 References 205 15 Power system alarm analysis and fault diagnosis using expert systems P. H. Ashmole 207 15. 1 Introduction 207 15. 2 Expert systems for power system alarm analysis already developed 208 15. 3 Existing substation control arrangements 209 15. 4 Discussion of alarm data flow 210 15. 5 Expert system requirements 210 15. 6 User interface 211 15. 7 Requirements under different fault conditions 211 15.
Nearly all our safety data collection and reporting systems are backwardlooking: incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where organizations have reduced incidents by employing predictive analytics to foresee and mitigate future risks. It discusses how Predictive Safety Analytics is an opportunity to break through the plateau problem where safety rate improvements have stagnated in many organizations. The book presents how the use of data, coupled with advanced analytical techniques, including machine learning, has become a proven and successful innovation. Emphasis is placed on how the book can “meet you where you are” by illuminating a path to get there, starting with simple data the organization likely already has. Highlights of the book are the real examples and case studies that will assist in generating thoughts and ideas for what might work for individual readers and how they can adapt the information to their particular situations. This book is written for professionals and researchers in system reliability, risk and safety assessment, quality control, operational managers in selected industries, data scientists, and ML engineers. Students taking courses in these areas will also find this book of interest to them.
Software safety is a crucial aspect during the development of modern safety-critical systems. However, safety is a system level property, and therefore, must be considered at the system-level to ensure the whole system’s safety. In the software development process, formal verification and functional testing are complementary approaches which are used to verify the functional correctness of software; however, even perfectly reliable software could lead to an accident. The correctness of software cannot ensure the safe operation of safety-critical software systems. Therefore, developing safety-critical software requires a more systematic software and safety engineering process that enables the software and safety engineers to recognize the potential software risks. For this purpose, this dissertation introduces a comprehensive safety engineering approach based on STPA for Software-Intensive Systems, called STPA SwISs, which provides seamless STPA safety analysis and software safety verification activities to allow the software and safety engineers to work together during the software development for safety-critical systems and help them to recognize the associated software risks at the system level.
Safety and Risk Modeling presents the latest theories and methods of safety and risk with an emphasis on safety and risk in modeling. It covers applications in several areas including transportations and security risk assessments, as well as applications related to current topics in safety and risk. Safety and Risk Modeling is a valuable resource for understanding the latest developments in both qualitative and quantitative methods of safety and risk analysis and their applications in operating environments. Each chapter has been written by active researchers or experienced practitioners to bridge the gap between theory and practice and to trigger new research challenges in safety and risk. Topics include: safety engineering, system maintenance, safety in design, failure analysis, and risk concept and modelling. Postgraduate students, researchers, and practitioners in many fields of engineering, operations research, management, and statistics will find Safety and Risk Modeling a state-of-the-art survey of reliability and quality in design and practice.
Decision making tools are essential for the successful outcome of any organization. Recent advances in predictive analytics have aided in identifying particular points of leverage where critical decisions can be made. Emerging Methods in Predictive Analytics: Risk Management and Decision Making provides an interdisciplinary approach to predictive analytics; bringing together the fields of business, statistics, and information technology for effective decision making. Managers, business professionals, and decision makers in diverse fields will find the applications and cases presented in this text essential in providing new avenues for risk assessment, management, and predicting the future outcomes of their decisions.
Covering a wide range of topics on safety, reliability and risk management, the present publication will be of interest to academics and professionals working in a wide range of scientific, industrial and governmental sectors, including: Aeronautics and Aerospace; Chemical and Process Industry; Civil Engineering; Critical Infrastructures; Energy; Information Technology and Telecommunications; Land Transportation; Manufacturing; Maritime Transportation; Mechanical Engineering; Natural Hazards; Nuclear Industry; Offshore Industry; Policy Making and Public Planning.