scholarly journals Tools for Risk Management of Technical Facilities Operation

2020 ◽  
Vol 5 (4) ◽  
pp. 494-500
Author(s):  
Dana Prochazkova ◽  
Jan Prochazka
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Management Plan ◽  
Life Cycles ◽  
Present Knowledge ◽  
Lessons Learned ◽  
Risk Management Plan ◽  
The Past

The article shows the results of research directed to detection of technical facilities accidents and failures sources at their operation. The research aim is to create the effective tools for management of risks so the coexistence of technical facilities with their vicinity would be ensured throughout their life cycles. The problems solution way is based on the simultaneously preferred concept, in which the safety is preferred over the reliability.  Respecting the present knowledge on technical facilities´ safety and the lessons learned from the past technical facilities accidents and  failures, the causes of which were connected with their operation, two tools are developed:  Decision Support System and Risk Management Plan that were reviewed by experts and tested in practice.

scholarly journals Risk-Based Design of Socio-Cyber-Physical Systems

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Dana Prochazkova ◽  
Jan Prochazka
Keyword(s):  
Risk Management ◽  
Management Plan ◽  
Cyber Physical Systems ◽  
Life Cycles ◽  
Present Knowledge ◽  
Risk Management Plan ◽  
Dynamic Changes ◽  
Physical Systems ◽  
The World

The aim of risk management of socio-cyber-physical systems at designing is the integral safety, which ensures their coexistence with their vicinity  throughout their life cycles. On the basis of present knowledge and experience, part of risks that threaten socio-cyber-physical systems shall be mitigated by preentive measures during their designing and manufacturing. Due to dynamic changes of the world, the conditions of socio-cyber-physical systems at operation change. If  changes exceed the socio-cyber-physical systems´ safety limits which were inserted into their designs, the accidents or  socio-cyber-physical sysems´ failures occur. The presented risk management plan is tool which ensures the prevention of such unaccepted situations and the safety.   

scholarly journals A Review of Information Fusion Methods for Gas Turbine Diagnostics

2019 ◽  
Vol 11 (22) ◽  
pp. 6202 ◽  
Author(s):  
Valentina Zaccaria ◽  
Moksadur Rahman ◽  
Ioanna Aslanidou ◽  
Konstantinos Kyprianidis
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Gas Turbine ◽  
Information Fusion ◽  
Support System ◽  
Probabilistic Reasoning ◽  
Decision Fusion ◽  
Lessons Learned ◽  
Diagnostic Systems ◽  
Fusion Methods

The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.

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