Gopika Vinod

Application of artificial neural networks to nuclear power plant transient diagnosis

A study on various artificial neural network (ANN) algorithms for selecting a best suitable algorithm for diagnosing the transients of a typical nuclear power plant (NPP) is presented. NPP experiences a number of transients during its operations. These transients may be due to equipment failure, malfunctioning of process systems, etc. In case of any undesired plant condition generally known as initiating event (IE), the operator has to carry out diagnostic and corrective actions. The objective of this study is to develop a neural network based framework that will assist the operator to identify such initiating events quickly and to take corrective actions. Optimization study on several neural network algorithms has been carried out. These algorithms have been trained and tested for several initiating events of a typical nuclear power plant. The study shows that the resilient-back propagation algorithm is best suitable for this application. This algorithm has been adopted in the development of operator support system. The performance of ANN for several IEs is also presented.

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

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.

An approach for early prediction of software reliability

In the early stages of development, failure information is not available to quantitatively measure reliability of a software product. In this context, we propose an approach to predict software reliability early in the product development stages from design metrics. First we predict reliabilities of the components of a system. For this, we categorize the different kinds of faults that can occur in a component during its development and identify the design metrics that correlate to these faults. We construct a Bayesian Belief Network (BBN) model to predict reliabilities of the components using the identified design metrics. Based on predicted reliabilities and usage frequencies of the components of a system, we determine the reliability of the system. The applicability of our proposed model is illustrated through a case study. Results obtained from our case study indicate the effectiveness of our approach

Software Reliability Early Prediction in Architectural Design Phase: Overview and Limitations

In recent times, computer based systems are frequently used for protection and control in the various industries viz Nuclear, Electrical, Mechanical, Civil, Electronics, Medical, etc. From the operating experience of those computer based systems, it has been found that the failure of which can lead to the severe damage to equipments or environmental harm. The culprit of this accident is nobody other than our software, whose reliability has not been ensured in those conditions. Also for real time system, throughput of the system and average response time are very important constructs/ metrics of reliability. Moreover neither of the software reliability model is available which can be fitted generically for all kinds of software. So, we can ensure reliability at the early stage i.e. during design phase by architecturing the software in a better way. The objective of this paper is to provide an overview of the state-of-the-art research in the area of architecture-based software reliability analysis. We then describe the shortcomings and the limiting assumptions underlying the prevalent research. We also propose various approaches which have the potential to address the existing limitations

A comprehensive framework for evaluation of piping reliability due to erosion–corrosion for risk-informed inservice inspection

Abstract Risk-Informed In-Service Inspection (RI-ISI) aims at prioritizing the components for inspection within the permissible risk level thereby avoiding unnecessary inspections. The two main factors that go into the prioritization of components are failure frequency and the consequence of the failure of these components. The study has been focused on piping component as presented in this paper. Failure frequency of piping is highly influenced by the degradation mechanism acting on it and these frequencies are modified as and when maintenance/ISI activities are taken up. In order to incorporate the effects of degradation mechanism and maintenance activities, a Markov model has been suggested as an efficient method for realistic analysis. Emphasis has been given to the erosion–corrosion mechanism, which is dominant in Pressurized Heavy Water Reactors. The paper highlights an analytical model for estimating the corrosion rates and also for finding the failure probability of piping, which can be further used in RI-ISI.

A technique for early prediction of software reliability based on design metrics

In the early stages of development, it is difficult to quantitatively assess the reliability of a software product. In this context, we propose a bottom-up approach to predict the reliability of an object-oriented software from its product metrics gathered during the architectural design stage. A fault model is constructed to categorize different kinds of faults that can occur in the components making up the software product. Subsequently, the product metrics collected during the software design phase are used to estimate the expected number of different kinds of faults that may occur in a component. Eventually, these estimated values of the different kinds of faults are used to predict the expected values of the total number of faults present in the component. We use the estimated fault content of the component and the number of tests that will be performed over the component, to predict reliability of the component. We adopt a probabilistic approach, Bayesian Belief Network, for reliability prediction of the components from product metrics. Based on predicted reliabilities and usage frequencies of the components, the reliability of a system is predicted. The applicability of our proposed model is illustrated through a case study. Moreover, we performed a set of experiments and also compared our approach with an established approach reported in the literature to investigate the accuracy of our approach. Analysis of the results from our experiments suggests that our approach yields reasonably accurate result.

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

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.

Reliability prediction of optocouplers for the safety of digital instrumentation

Conventionally, reliability prediction of electronic components is carried out using standard handbooks such as MIL STD 217plus, Telecordia, etc. But these methods fail to provide a realistic estimate of reliability for upcoming technologies. Currently, electronic reliability prediction is moving towards applying the Physics of Failure approach which considers information on process, technology, fabrication techniques, materials used, etc. Industries employ different technologies like CMOS, BJT and BICMOS for various applications. The possibility of chance of failure at interdependencies of materials, processes, and characteristics under operating conditions is the major concern which affects the performance of the devices. They are characterized by several failure mechanisms at various stages such as wafer level, interconnection, etc. For this, the dominant failure mechanisms and stress parameters needs to be identified. Optocouplers are used in input protection of several instrumentation systems providing safety under over-stress conditions. Hence, there is a need to study the reliability and safety aspects of optocouplers. Design of experiments is an efficient and prominent methodology for finding the reliability of the item, as the experiment provides a proof for the hypothesis under consideration. One of the important techniques involved is Tagauchi method which employs for finding the prominent failure mechanisms in semiconductor devices. By physics of failure approach, the factors that are affecting the performance on both environmental and electrical parameters with stress levels for optocouplers are identified. By constructing a 2-stage tagauchi array with these parameters where output parameters decides the effect of top two dominant failure mechanisms and their extent of chance of failure can be predicted. This analysis helps us in making the appropriate modifications considering both the failure mechanisms for the reliability growth of these devices. This paper highlights the application of design of experiments for finding the dominant failure mechanisms towards using physics of failure approach in electronic reliability prediction of optocouplers for application of instrumentation.

Early Prediction of Software Reliability: A Case Study with a Nuclear Power Plant System

Existing methods to predict software reliability using the Markov chain are based on assumed state-transition probabilities. A new prediction approach applied to a nuclear plants feed-water system yielded results that were 96.9 percent accurate relative to the systems actual reliability. Across 38 operational datasets, the average accuracy was 99.67 percent.

Design Verification of Instrumentation and Control Systems of Nuclear Power Plants

Instrumentation and Control systems are the nervous system of a nuclear power plant. They monitor all facets of the plants health and help respond with care and adjustments needed, thus ensuring goals of efficient power production and safety. Due to safety significance of I&C, it becomes increasingly important to have a design verification methodology which ensures I&C systems fully functional. The strategy discussed the system modeling for design verification using Petri Net, converting it into Markov Chain and solving the linear system mathematically. It also exploits the best attribute of the created Markov model. The approach has been validated on seven sets of operation profile data of reactor control system of seven Nuclear Power Plants. The singular & plural of an acronym are always spelled the same.