Gilberto F. M. Souza

Bayesian Networks on Risk Analysis of a Regasification System on an Offshore Unit

Nowadays, LNG Import Terminals (where the storage and regasification process is conducted) are mostly onshore; the construction of these terminals is costly and many adaptations are necessary to abide by environmental and safety laws. Moreover, an accident in one of these plants might produce considerable impact in neighboring areas and population; this risk may be even worse due to the possibility of a terrorist attack. Under this perspective, a discussion is conducted about a vessel known as FSRU (Floating Storage and Regasification Unit), which is a storage and regasification offshore unit, that can work miles away from de coast and, owing to this, can be viewed as an option for LNG storage and regasification facilities. The goal is to develop a method for using Bayesian Networks in the Risk Analysis of Regasification System of the FSRU, which will convert Fault Trees (FT) into Bayesian Networks (BN) providing more accurate data. Using BN is possible to represent uncertain knowledge and local conditional dependencies. In addition, FT models the failure modes as independent and binary events while BN may model a larger number of states. It is worth noting that BN does not require the determination of cut sets; however, given a failure, it is capable of providing the probability of each possible cut set. This method will provide information to define, in a future study, a maintenance plan based on the Reliability Centered Maintenance. The results intend to clarify the applicability of BN on risk assessment and might improve the risk analysis of a Regasification System FSRU.Copyright

Equipment failure prediction based on neural network analysis incorporating maintainers inspection findings

The improvement of condition monitoring of critical equipment of complex systems supports the development of predictive maintenance planning. The main goal of predictive maintenance is to understand how the component degradation is affecting the performance of equipment. Based on performance degradation, the maintenance planner defines the necessity for intervention on the equipment. For complex systems, the monitored operational parameters may be used to support predictive maintenance decision-making. The combination of the parameters registered during operational campaign can be used to define the normal or abnormal equipment performance. The present paper presents a reliability-based methodology to support decision-making regarding operational performance of equipment. Based on FMEA analysis, the critical components for equipment performance are defined. The monitoring equipment is analyzed aiming at defining the registered parameters that can be used to evaluate critical components degradation associated to specific failure modes. In case of absence of monitoring parameters, an inspection route is proposed to be executed by maintainers. The main goal of the route is to use sensorial capabilities of human being to evaluate possible degradation of critical components that are not monitored by the control system. The findings of that inspection route may be used to support the evaluation of any abnormal component operational condition that may affect equipment performance. In order to combine the parameters registers and sensorial inspection results to diagnose degradation in equipment performance a Neural Network based on Radial Basis Function is developed. For the definition of normal or abnormal operational condition the decision-maker preference is modeled with utility functions that incorporate attitude toward risk. The proposed model is used to analyze the operational condition of a hydro generator. The parameters of temperature (from bearings and heat exchangers) and shaft vibration are used to characterize hydro generator operation. For other critical equipment a sensorial inspection route is proposed using mainly temperature information to support components degradation analysis. The registered and sensorial data representing one operational month are used to train the neural network regarding normal and abnormal equipment condition. After training, the Neural Network was able to detect abnormal operational condition. The use of utility functions incorporating operator attitude towards risk indicates that the definition of abnormal performance is strongly affected by operators preference.

Reliability modeling of partially repairable systems applied on electrical power system

Partially repairable systems have components with repairable and non-repairable failure modes, which is typical of mission-oriented or safety systems, where incomplete repairs for the operation scenario could take place. Analytical and numerical solutions for reliability modeling of partially repairable systems are proposed in this work. The proposed modeling, based on Markov chains, includes stand-by redundancy and a repair rate factor. Reasons for limited repair capacity are discussed. The modeling is applied to reliability analysis of an electrical power system typical of Nuclear Power Plants, considered to be composed of offsite power lines and redundant Diesel-generators aiming at analyzing an electrical power outage (blackout). A sensitivity analysis of reparability rate impact on availability and reliability is performed, demonstrating the gain resulting from the repair capacity increase.

Decision-Making Model for Offshore Offloading Operations Based on Probabilistic Risk Assessment

This paper presents a risk-based analysis method aiming at defining the risk profile associated with an offloading operation. For offloading operations the risk profile is usually evaluated considering that the environmental condition will not suffer considerable changes during offloading that has an approximate duration of 24 hours, varying based on the tanker size. The method follows four basic steps: Accident Modeling, Failure probability assessment with Bayesian techniques, Evaluation of consequences, and Markovian process to aid decision making. The method is applied to evaluate the risk profile of an offloading operation in Campos Basin, Brazil. The method is used to model the risk scenario associated with shuttle tanker main engine failure as initiating event. The changes in environmental conditions have great influence in risk profile and increase the probability of disconnection.

Reliability concepts applied to manufacturing processes planning

We present a reliability analysis of a precision drilling process used in the manufacturing of aircraft structural components. After a brief introduction to the precision drilling manufacturing process, a reliability analysis is executed through the application of the FMEA technique, defining the process main failures and their consequences to the manufactured part. The statistical process control capability indices are proposed as estimates of the process failure rate. To evaluate the process failure rate and capability an experimental study is developed with test specimens. Based on the test results, the drilling procedure for aluminum is accepted and the procedure for titanium is rejected