Generating a Risk Profile for Car Insurance Policyholders: A Deep Learning Conceptual Model

In recent years, technological improvements have provided a variety of new opportunities for insurance companies to adopt telematics devices in line with usage-based insurance models. This paper sheds new light on the application of big data analytics for car insurance companies that may help to estimate the risks associated with individual policyholders based on complex driving patterns. We propose a conceptual framework that describes the structural design of a risk predictor model for insurance customers and combines the value of telematics data with deep learning algorithms. The model’s components consist of data transformation, criteria mining, risk modelling, driving style detection, and risk prediction. The expected outcome is our methodology that generates more accurate results than other methods in this area

An intelligent system by fuzzy reliability algorithm in fault tree analysis for nuclear power plant probabilistic safety assessment

© Imperial College Press. Fault tree analysis for nuclear power plant probabilistic safety assessment is an intricate process. Personal computer-based software systems have therefore been developed to conduct this analysis. However, all existing fault tree analysis software systems only accept quantitative data to characterized basic event reliabilities. In real-world applications, basic event reliabilities may not be represented by quantitative data but by qualitative justifications. The motivation of this work is to develop an intelligent system by fuzzy reliability algorithm in fault tree analysis, which can accept not only quantitative data but also qualitative information to characterized reliabilities of basic events. In this paper, a newly-developed system called InFaTAS-NuSA is presented and its main features and capabilities are discussed. To benchmark the applicability of the intelligent concept implemented in InFaTAS-NuSA, a case study is performed and the analysis results are compared to the results obtained from a well-known fault tree analysis software package. The results confirm that the intelligent concept implemented in InFaTAS-NuSA can be very useful to complement conventional fault tree analysis software systems

KUANTIFIKASI KETIDAKPASTIAN PADA ANALISIS POHON KEGAGALAN DENGAN PENDEKATAN FUZZY

Analisis pohon kegagalan dipakai untuk mengevaluasi kinerja sistem keselamatan pembangkit listrik tenaga nuklir. Analisis ini memerlukan ketersediaan data kegagalan komponen. Karena keandalan komponen dipengaruhi oleh lingkungan kerjanya maka perlu digunakan data kegagalan komponen yang berasal dari sistem yang sedang dievaluasi. Namun kenyataannya, data ini sangat sulit diperoleh sehingga penggunaan data jenerik menjadi tak terhindarkan. Penggunaan data jenerik tentunya akan menyebabkan ketidakpastian pada hasil analisis. Simulasi Monte Carlo sering dipakai untuk mengkuantifikasi ketidakpastian ini. Namun sebenarnya metode ini kurang tepat untuk mengevaluasi ketidakpastian apabila jumlah data yang dimiliki sangat terbatas. Tujuan dari penelitian ini adalah pengembangan sebuah metode analisis pohon kegagalan baru yang menerapkan konsep fuzzy untuk kuantifikasi ketidakpastian. Dalam metode baru ini, probabilitas fuzzy dipakai untuk merepresentasikan probabilitas kejadian dasar, antara serta puncak dan hukum kombinasi fuzzy dipakai untuk mengevaluasi ketidakpastian hasil analisis. Kebolehjadian gagalnya sistem injeksi akumulator AP1000 telah dievaluasi dengan menggunakan metode baru ini dan diperoleh ketidakpastian kegagalan pada interval 8,87E-12 – 8,87E-8 dengan nilai titik tengah 8,87E-10. Hasil ini membuktikan bahwa analisis pohon kegagalan dengan pendekatan fuzzy ini layak dipakai apabila yang menjadi fokus evaluasi adalah ketidakpastian karena keterbatasan data kegagalan yang dimiliki.Kata kunci: Analisis pohon kegagalan, analisis ketidakpastian, probabilitas fuzzy, hukum kombinasi fuzzy Fault tree analysis has been applied to evaluate nuclear power plant safety systems. To perform this analysis, component reliabilities need to be provided well in advance. Since working environment can affect component reliability, it is necessary to directly collect such data from the safety system being evaluated. However, due to lack of resources, such data may be unattainable. Hence, the use of generic data cannot be avoided. Unfortunately, generic data will add uncertainty to the analysis. Monte Carlo simulation has been performed to evaluate such uncertainty. However, this method is not appropriate when components do not have probability distributions of their lifetime to failures. The aim of this study is to propose a new fault tree analysis method which implements fuzzy concepts for quantifying such uncertainty. In the proposed method, fuzzy probabilities represent basic, intermediate as well as top event probabilities and fuzzy combination rules are used to evaluate the overall uncertainty of the fault tree. The proposed method has been performed to evaluate failure probability of the AP1000 accumulator injection system and generate a probability distribution between 8.87E-12 and 8.87E-8 with the point median value of 8.87E-10. This result confirms that the proposed method is feasible to evaluate system fault tree when uncertainty raised by the lack of reliability data is the main focus of the analysis.Keywords: Fault tree analysis, uncertainty analysis, fuzzy probabilities, fuzzy combination rule

KUANTIFIKASI KETIDAKPASTIAN PADA ANALISIS POHON KEGAGALAN DENGAN PENDEKATAN FUZZY

Analisis pohon kegagalan dipakai untuk mengevaluasi kinerja sistem keselamatan pembangkit listrik tenaga nuklir. Analisis ini memerlukan ketersediaan data kegagalan komponen. Karena keandalan komponen dipengaruhi oleh lingkungan kerjanya maka perlu digunakan data kegagalan komponen yang berasal dari sistem yang sedang dievaluasi. Namun kenyataannya, data ini sangat sulit diperoleh sehingga penggunaan data jenerik menjadi tak terhindarkan. Penggunaan data jenerik tentunya akan menyebabkan ketidakpastian pada hasil analisis. Simulasi Monte Carlo sering dipakai untuk mengkuantifikasi ketidakpastian ini. Namun sebenarnya metode ini kurang tepat untuk mengevaluasi ketidakpastian apabila jumlah data yang dimiliki sangat terbatas. Tujuan dari penelitian ini adalah pengembangan sebuah metode analisis pohon kegagalan baru yang menerapkan konsep fuzzy untuk kuantifikasi ketidakpastian. Dalam metode baru ini, probabilitas fuzzy dipakai untuk merepresentasikan probabilitas kejadian dasar, antara serta puncak dan hukum kombinasi fuzzy dipakai untuk mengevaluasi ketidakpastian hasil analisis. Kebolehjadian gagalnya sistem injeksi akumulator AP1000 telah dievaluasi dengan menggunakan metode baru ini dan diperoleh ketidakpastian kegagalan pada interval 8,87E-12 – 8,87E-8 dengan nilai titik tengah 8,87E-10. Hasil ini membuktikan bahwa analisis pohon kegagalan dengan pendekatan fuzzy ini layak dipakai apabila yang menjadi fokus evaluasi adalah ketidakpastian karena keterbatasan data kegagalan yang dimiliki.Kata kunci: Analisis pohon kegagalan, analisis ketidakpastian, probabilitas fuzzy, hukum kombinasi fuzzy Fault tree analysis has been applied to evaluate nuclear power plant safety systems. To perform this analysis, component reliabilities need to be provided well in advance. Since working environment can affect component reliability, it is necessary to directly collect such data from the safety system being evaluated. However, due to lack of resources, such data may be unattainable. Hence, the use of generic data cannot be avoided. Unfortunately, generic data will add uncertainty to the analysis. Monte Carlo simulation has been performed to evaluate such uncertainty. However, this method is not appropriate when components do not have probability distributions of their lifetime to failures. The aim of this study is to propose a new fault tree analysis method which implements fuzzy concepts for quantifying such uncertainty. In the proposed method, fuzzy probabilities represent basic, intermediate as well as top event probabilities and fuzzy combination rules are used to evaluate the overall uncertainty of the fault tree. The proposed method has been performed to evaluate failure probability of the AP1000 accumulator injection system and generate a probability distribution between 8.87E-12 and 8.87E-8 with the point median value of 8.87E-10. This result confirms that the proposed method is feasible to evaluate system fault tree when uncertainty raised by the lack of reliability data is the main focus of the analysis.Keywords: Fault tree analysis, uncertainty analysis, fuzzy probabilities, fuzzy combination rule

A fuzzy Bayesian network approach for risk analysis in process industries

YesFault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant

Three Dimensional Fuzzy Reliability for System Performance Evaluation

The research proposed a developed methodology for evaluation the system performance in uncertainty associated with traditional modelling methodology is focused on either load L or resistance R variability, but not both. A two-dimensional (2D) fuzzy set (traditional model), represent with the one dimension for universe of discourse (in x-direction) and the second dimension of his membership degree (in y-direction), is not full sufficient to handle both, load and resistance variation of system performance. The theoretical principle basis of this research is based on development of the three dimensional (3D) of fuzzy set that includes system performance variability in load and resistance from two dimensional. The proposed methodology (traditional model) extends the acceptance level of partial performance of system concept to a 3D-dimantion representation. This representation allows to capturing the changing of preferences of decision makers in load and resistance. The major objective of the research is to proposed the original methodology for evaluate system performance and management that is capable of; (a) addressing uncertainty caused by load and resistance variability and ambiguity; (b) integrating objective and subjective evaluation; and (c) assisting system performance management decision making based on a more detailed certainty evaluation of load and resistance variability. The study proposed two models for fuzzy reliability performance indexes: first traditional model included (I) 2D fuzzy reliability-vulnerability Rv index, (II) 2D fuzzy robustness Ro index; the second developed model (i) 3D fuzzy reliability-vulnerability Rv index, (ii)  3D fuzzy robustness Ro index; and comparing between them. These indexes have the capability of evaluating the operational performance of complex systems. Proposed methodology is illustrated by using the Al-Wathba Water Supply System (WWSS) as a case study

A review of applications of fuzzy sets to safety and reliability engineering

Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering

Fuzzy evidence theory and Bayesian networks for process systems risk analysis

YesQuantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.The research of Sohag Kabir was partly funded by the DEIS project (Grant Agreement 732242)

A fuzzy reliability assessment of basic events of fault trees through qualitative data processing

Probabilistic approaches are common in the analysis of reliability of complex engineering systems. However, they require quantitative historical failure data for determining reliability characteristics. In many real-world areas, such as e.g., nuclear engineering, quantitative historical failure data are unavailable or become inadequate and only qualitative data such as expert opinions, which are described in linguistic terms, can be collected and then used to assess system reliability. Moreover, experts are more comfortable justifying event failure likelihood using linguistic terms, which capture uncertainties rather than by expressing judgments in a quantitative manner. New techniques are therefore needed that will help construct models of reliability of complex engineering system without being confined to quantitative historical failure data. The objective of this study is to develop a fuzzy reliability algorithm to effectively generate basic event failure probabilities without reliance on quantitative historical failure data through qualitative data processing. The originality of this study comes with an introduction of linguistic values articulated in terms of component failure possibilities in order to qualitatively assess basic event failure possibilities treated as inputs of the proposed model and generate basic event failure probabilities as its outputs. To demonstrate the feasibility and effectiveness of the proposed algorithm, actual basic event failure probabilities collected from nuclear power plant operating experiences are compared with the failure probabilities generated by the algorithm. The results demonstrate that the proposed fuzzy reliability algorithm arises as a suitable alternative for the probabilistic reliability approach when quantitative historical failure data are unavailable. © 2013 Elsevier B.V