A. Srividya

Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment

Traditional fault tree (FT) analysis is widely used for reliability and safety assessment of complex and critical engineering systems. The behavior of components of complex systems and their interactions such as sequence- and functional-dependent failures, spares and dynamic redundancy management, and priority of failure events cannot be adequately captured by traditional FTs. Dynamic fault tree (DFT) extend traditional FT by defining additional gates called dynamic gates to model these complex interactions. Markov models are used in solving dynamic gates. However, state space becomes too large for calculation with Markov models when the number of gate inputs increases. In addition, Markov model is applicable for only exponential failure and repair distributions. Modeling test and maintenance information on spare components is also very difficult. To address these difficulties, Monte Carlo simulation-based approach is used in this work to solve dynamic gates. The approach is first applied to a problem available in the literature which is having non-repairable components. The obtained results are in good agreement with those in literature. The approach is later applied to a simplified scheme of electrical power supply system of nuclear power plant (NPP), which is a complex repairable system having tested and maintained spares. The results obtained using this approach are in good agreement with those obtained using analytical approach. In addition to point estimates of reliability measures, failure time, and repair time distributions are also obtained from simulation. Finally a case study on reactor regulation system (RRS) of NPP is carried out to demonstrate the application of simulation-based DFT approach to large-scale problems.

Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies

There will be simplifying assumptions and idealizations in the availability models of complex processes and phenomena. These simplifications and idealizations generate uncertainties which can be classified as aleatory (arising due to randomness) and/or epistemic (due to lack of knowledge). The problem of acknowledging and treating uncertainty is vital for practical usability of reliability analysis results. The distinction of uncertainties is useful for taking the reliability/risk informed decisions with confidence and also for effective management of uncertainty. In level-1 probabilistic safety assessment (PSA) of nuclear power plants (NPP), the current practice is carrying out epistemic uncertainty analysis on the basis of a simple Monte-Carlo simulation by sampling the epistemic variables in the model. However, the aleatory uncertainty is neglected and point estimates of aleatory variables, viz., time to failure and time to repair are considered. Treatment of both types of uncertainties would require a two-phase Monte-Carlo simulation, outer loop samples epistemic variables and inner loop samples aleatory variables. A methodology based on two-phase Monte-Carlo simulation is presented for distinguishing both the kinds of uncertainty in the context of availability/reliability evaluation in level-1 PSA studies of NPP.

Reliability and Safety Engineering

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Multi-Sensor Data Fusion in Cluster based Wireless Sensor Networks Using Fuzzy Logic Method

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Test interval optimization of safety systems of nuclear power plant using fuzzy-genetic approach

Wireless sensor network (WSN) consist of a large number of sensor nodes which are limited in battery power and communication range and are having multi-modal sensing capability. One of the most significant applications of wireless sensor network is environment monitoring. In this paper, a multi-sensor data fusion algorithm in WSN using fuzzy logic for event detection application is proposed. In the proposed method, each sensor node is equipped with diverse sensors (temperature, humidity light, and carbon monoxide). The use of more than one sensor provides additional information on the environmental condition. The processing and fusion of these diverse sensor signals are carried out using proposed fuzzy rule based system. All the diverse sensor signals are collected at the cluster head and fused using fuzzy rule based method. The multiple data fusion process improves the reliability and accuracy of the sensed information and thereby minimizes the false alarm rate.

Optimisation of ISI interval using genetic algorithms for risk informed in-service inspection

Abstract Probabilistic safety assessment (PSA) is the most effective and efficient tool for safety and risk management in nuclear power plants (NPP). PSA studies not only evaluate risk/safety of systems but also their results are very useful in safe, economical and effective design and operation of NPPs. The latter application is popularly known as “Risk-Informed Decision Making”. Evaluation of technical specifications is one such important application of Risk-Informed decision making. Deciding test interval (TI), one of the important technical specifications, with the given resources and risk effectiveness is an optimization problem. Uncertainty is inherently present in the availability parameters such as failure rate and repair time due to the limitation in assessing these parameters precisely. This paper presents a solution to test interval optimization problem with uncertain parameters in the model with fuzzy-genetic approach along with a case of application from a safety system of Indian pressurized heavy water reactor (PHWR).

Response-Time Modeling of Controller Area Network (CAN)

Abstract Risk Informed In-Service Inspection (RI-ISI) aims at prioritising the components for inspection within the permissible risk level thereby avoiding unnecessary inspections. Various constraints such as risk level, radiation exposure to the workers and cost of inspections are encountered, while planning the inspection programme. This problem has been attempted to solve using genetic algorithms, which has already established its suitability in optimizing Surveillance and Maintenance activities in Nuclear Power Plants. The paper describes the application of genetic algorithm in optimizing the ISI of feeders, which are large in number and also fall in the same inspection category.

Modeling demand rate and imperfect proof-test and analysis of their effect on system safety

A probabilistic approach to determine response-time distribution for messages in Controller Area Network (CAN) is presented here. CAN is a field bus level communication network for exchanging short real-time messages. CAN is mostly used to carry periodic messages for control and automation systems. Traditional response-time analysis of CAN messages only gives worst-case response-times. This introduces pessimism in analysis, resulting in over designing and under utilization of resources. Response-time distribution is a probabilistic function. Response-time distribution provides a complete characterization of CAN message response-time. It can provide probability of meeting/missing any specified response-time limit. This probability is useful for system reliability and performance modeling. In this paper response-time model of CAN messages is discussed. Technique to derive probabilistic parameters of response-time model, for periodic CAN messages with deterministic transmission times is given. The method has been applied to a standard data set, results are compared with that of worst-case analysis and reasons for deviations are discussed.

Importance measures in ranking piping components for risk informed in-service inspection

Abstract Quantitative safety assessment of a safety system plays an important role in comparing design alternatives at design stage and deciding appropriate design options to apply for safety systems. There are a number of such indices given in the literature. Most of the safety indices consider only system parameters (hazard rate, repair rate, diagnosis, coverage, etc.) along with proof-tests (or inspection). This paper extends the underlying model to incorporate demand rate and imperfect proof-tests. It also introduces a new safety index, average probability of failure on actual demand (PFaD), and an availability index, manifested availability (mAv). This paper uses Markov regenerative process-based analysis for state probabilities. Based on state-probability values of various states of the underlying Markov chain, solutions are derived for safety index PFaD and availability mAv.

Fuzzy Based Optimized Routing Protocol for Wireless Sensor Networks

Abstract Risk informed in-service inspection aims at prioritising the components for inspection within the permissible risk level thereby avoiding unnecessary inspections. Various methods have been evolved for prioritisation, in which the importance measure approach has gained a wide popularity. This paper presents an importance measure that can be employed to prioritise components of any dimension, which is normally required from the point of view of carrying out a risk informed in-service inspection of nuclear power plants.