Probability Estimation Method Research Articles

A probabilistic model for fatality estimation of ship fire accidents

Fatality estimation is beneficial for improving the safety of fireproofing in ship design and ensuring a quick response to fire accidents. This paper proposes a probabilistic method for fatality estimation of fire accidents caused by critical temperature and critical smoke in terms of comparing the available safe egress time and the required safe egress time. The kernel of this proposed method is first to derive the available safe egress time estimation equation by using fire dynamics simulator field model to simulate the fire development process and, to determine the required safe egress time equation given by the guideline of International Maritime Organization, which consider crowd behaviours, including waiting time at corridors, stairs and doors. The proposed method is applied to a real fire accident and the fatality rate is approximate to real scenarios. Consequently, this paper proposes a practical and holistic method for fatality estimation of ship fire accidents.

Probabilistic multi-aircraft conflict detection approach for trajectory-based operation

Conflict detection (CD) is one of the key functions used to ensure air transport safety and efficiency. In trajectory-based operation (TBO), aircraft are provided with more flexibility in en route trajectory planning and more responsibility for self-separation. The high flexibility in trajectory planning enables random changes in pilot intent, thus increasing the uncertainty in trajectory prediction and CD. This study proposes a novel probabilistic CD approach for TBO in which the uncertainty of pilot intent is taken into account by quantifying the aircraft reachable domain constrained by the flight plan. First, a probabilistic model for aircraft trajectory prediction is developed using the truncated Brownian bridge method. Based on this model, a novel conflict probability estimation method is developed. Finally, the performance of the proposed probabilistic CD approach is demonstrated through an illustrative air traffic scenario.

Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System

As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg–Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.

An integrated risk assessment of onshore gas transmission pipelines based on defect population

The aim of this research is to develop an original probabilistic method for an underground onshore gas transmission pipelines maintenance risk estimation. The method considers a population of the anomalies detected by an in-line inspection . In the methodology, features are considered as a family with certain defect depth and shape distribution derived from the repeated in-line diagnostics of a steel pipeline under operation. A growth rate in the radial direction is determined based on a defects size change between two or more inspections. The number of non-detected hypothetical anomalies were taken into account due to probability of detection and sizing uncertainties by means of a non-perfect inspection device. Metal losses of the pipe wall are assessed by a fracture mechanics method based on a limit load theory and considered as a leak or rupture. Gas pressure distribution and temperature along the pipeline are calculated in steady state conditions. Particular attention was paid to the evaluation of the probabilistic pipeline failure consequences to the surrounding area with parameters as population density and road traffic taken into consideration. Although the presented risk estimation is universally applicable, the novel methodology was applied to the high pressure gas transmission pipeline situated in the area similar conditions typical for Europe. The failure probability calculations are carried out for different pipeline modes of the fluid transportation. A significant connection of the risk evaluation with pressure distribution along the pipeline was observed based on the differences in risk distributions between Summer, Autumn and Winter operational modes.

Steel Statically Uncertain Transverse Frames Probabilistic Calculation

The aim of the work is probabilistic calculation and evaluation of steel statically uncertain frames reliability parameters and recommendations development for reducing their material content by controlling the reserves of the load bearing capacity of the.A method for evaluating the reliability parameters of steel statically uncertain frames by all able-bodied statens is developed. Logical probabilistic methods of statically uncertain systems work are used. The probability estimation method of statically uncertain systems failure based on the boundary equilibrium method, taking into account the correlation of individual destruction schemes, is obtained. The method of calculating the significance and contribution of individual frame elements and their influence on the system failure probability as a whole is proposed. The practical method of designing steel statically unidentified frames with the use of steel statically uncertain constructions reliability coefficient γs is offered. The scientific novelty of the work is to develop a scheme for determining frames failure probability by estimating the working capacity. In particular, the fragile and plastic fracturing failures of the frame section are considered. The algorithms for calculating the steel frames failure probability are determined by the probabilistic method of graphical equilibrium. The steel frame separate elements significance indexes are determined. The numerical experiment on the developed programs on a PC is presented for estimation of steel frames failure probability of various sections. the of of steel statically uncertain constructions reliability coefficient γs is proposed for usage.The practical value is more complete consideration of the structures and the probabilistic nature of strength and load, the use of the reliability coefficient. The possibilities of obtaining more economical frames in the design of new ones as well as reconstruction of existing buildings and facilities are identified. Based on the proposed methodology, algorithms and developed programs on the PC for the probabilistic calculation of steel statically uncertain frames for various purposes are compiled.

Prognostic Health Monitoring Method for Fatigue Failure of Solder Joints on Printed Circuit Boards Based on a Canary Circuit

Devices mounted on printed circuit boards (PCBs) are subject to temperature variations resulting from power switching and ambient temperature changes, and may be subject to random dynamic load histories from sources such as vibration. Since solder material is mechanically the most ductile part, fatigue failure may occur in solder joints. Health monitoring for fatigue life under field conditions is a key issue for improving availability and serviceability for maintenance. We have developed a failure precursor detection technology and a fatigue life estimation method for ball grid array (BGA) solder joints, based on a canary circuit. This method estimates fatigue failure life of an actual circuit by detecting failure connections in a canary circuit (a dummy circuit of daisy-chained solder joints). The canary circuit is designed to fail before the actual circuit under the same failure mode by using accelerated reliability testing and inelastic stress simulation. A feasibility study of the failure probability estimation method is conducted by applying the method to a PCB on which a BGA component is mounted. It is confirmed that the fatigue life under a thermal cyclic load can be estimated from a canary circuit, that estimation of fatigue life under a random dynamic load is feasible, and that the estimation results are consistent with results from actual random vibration tests. The proposed method is found to be useful for prognostic health monitoring of solder joint fatigue failure.

A top-down iteration algorithm for Monte Carlo method for probability estimation of a fault tree with circular logic

Abstract Calculating minimal cut sets is a typical quantification method used to evaluate the top event probability for a fault tree. If minimal cut sets cannot be calculated or if the accuracy of the quantification result is in doubt, the Monte Carlo method can provide an alternative for fault tree quantification. The Monte Carlo method for fault tree quantification tends to take a long time because it repeats the calculation for a large number of samples. Herein, proposal is made to improve the quantification algorithm of a fault tree with circular logic. We developed a top-down iteration algorithm that combines the characteristics of the top-down approach and the iteration approach, thereby reducing the computation time of the Monte Carlo method.

Efficient method for probabilistic estimation of spatially varied hydraulic properties in a soil slope based on field responses: A Bayesian approach

An efficient probabilistic back estimation method for characterization of spatial variability is proposed by integration of the Karhunen–Loève (K-L) expansion method, the Polynomial Chaos Expansion (PCE) method and the Markov Chain Monte Carlo (MCMC) method. To reduce the dimension of back estimation, the spatially varied soil property is simulated using the K-L expansion method and the basic random variables of K-L terms are parameters to be estimated. To further reduce computation load, a PCE surrogate model is constructed to substitute the original model. The proposed method is applied on an example where a randomly heterogeneous soil slope is subject to surface infiltration. The pressure responses are used to estimate the spatial variability of the saturated coefficient permeability. The results show that the spatial variability can be satisfactorily estimated. The coefficient of variation of the estimation is less than 5%.

Fast Searching Strategy for Critical Cascading Paths Toward Blackouts

The analysis of severe cascading blackouts is an essential issue in power grid planning and operation. As there are a tremendous number of possible cascade paths in a large power system, it is very challenging to determine the critical cascading paths that may have catastrophic consequences. This paper proposes a complex network theory based methodology to determine such critical cascading paths with high efficiency. To this end, we construct an improved interaction graph to cope with the situation where the $(N-1)$ criterion is not satisfied during the follow-up cascade propagation after an initial fault. Subsequently, based on the graph, we derive a modified PageRank model to fast rank the influence of individual transmission lines on blackout risks. Further, leveraging the results of influence ranking, we devise an efficient strategy for searching critical cascading paths. Then, we derive an unbiased probability estimation method for individual cascading paths and the blackout. Simulations carried out on the IEEE 39-bus system, the IEEE 118-bus system, and a real-world 1122-bus system in China show that our method can enhance the searching efficiency by up to three orders of magnitude compared with standard Monte Carlo approaches, demonstrating its potential for cascading blackout analysis in large-scale power systems. Results also verify that the proposed probability estimation method is unbiased, and it can provide an efficient way for probability estimation of the blackouts with very low probability but high losses.

Collision probability prediction algorithm for cooperative overtaking based on TTC and conflict probability estimation method

This paper focuses on the feasibility and methodology of critical issues of overtaking behaviour using inter-vehicle communication technology. The main innovation of this work is the integration of a collision probability model with the overtaking procedure. In detail, the overtaking process was divided into two successive stages: the approaching stage and surpassing stage. In the approaching stage, linear assumption between collision probability and time to collision (TTC) was proposed to release the computational load. While, in the surpassing stage, conflict probability estimation method is utilised as the safety quantitative indicator to estimate the collision probability. To support the proposed method, we run many experiments on synthetic data. The result shows that our method achieves better accuracy especially in unpredictable nature of road environment, including the capability of making correct behaviour decisions and avoiding collisions with nearby vehicles, which make the proposed method sufficient for real-world implementation.

Collision probability prediction algorithm for cooperative overtaking based on TTC and conflict probability estimation method

This paper focuses on the feasibility and methodology of critical issues of overtaking behaviour using inter-vehicle communication technology. The main innovation of this work is the integration of a collision probability model with the overtaking procedure. In detail, the overtaking process was divided into two successive stages: the approaching stage and surpassing stage. In the approaching stage, linear assumption between collision probability and time to collision (TTC) was proposed to release the computational load. While, in the surpassing stage, conflict probability estimation method is utilised as the safety quantitative indicator to estimate the collision probability. To support the proposed method, we run many experiments on synthetic data. The result shows that our method achieves better accuracy especially in unpredictable nature of road environment, including the capability of making correct behaviour decisions and avoiding collisions with nearby vehicles, which make the proposed method sufficient for real-world implementation.

Stochastic Knock Control with Beta Distribution Learning for Gasoline Engines

Knock phenomenon as a stochastic process requires feedback control for its relation to engine efficiency, noise and cylinder damage. In this paper, a knock probability estimation method using Bayes’ updating rule and beta distribution is proposed based on the independent and identically distributed (iid) characteristic analysis of the knock event sequence. A stochastic control algorithm using the estimation method and likelihood ratio test is also proposed. The proposed control algorithm is validated on a production spark ignition engine and shows the ability to maintain knock probability close to the target.

Probabilistic methods for estimation of the extreme value statistics of ship ice loads

It is well known that when a ship sails in ice-covered regions, the ship-ice interaction process is complex and the associated ice loads on the hull is a stochastic process. Therefore, statistical models and methods should be applied to describe the ice load process. The aim of this work is to present a novel method for estimating the extreme ice loads which is directly related to the reliability of the vessel. This method, briefly referenced to as the ACER (average conditional exceedance rate) method, can provide a reasonable extreme value prediction of the ice loads by efficiently utilizing the available data, which was collected by an ice load monitoring (ILM) system. The basic idea for the ACER approach lies in the fact that a sequence of nonparametric distribution functions are constructed in order to approximate the extreme value distribution of the collected time history. The main principle of the ACER method is presented in detail. Furthermore, the methods based on the classic extreme value theory are also introduced in order to provide a benchmark study.

Assembly yield prediction of plastically encapsulated packages with a large number of manufacturing variables by advanced approximate integration method

An advanced approximate integration scheme called eigenvector dimension reduction (EDR) method is implemented to predict the assembly yield of a plastically encapsulated package. A total of 12 manufacturing input variables are considered during the yield prediction, which is based on the JEDEC reflow flatness requirements. The method calculates the statistical moments of a system response (i.e., warpage) first through dimensional reduction and eigenvector sampling, and a probability density function (PDF) of random responses is constructed subsequently from the statistical moments by a probability estimation method. Only 25 modeling runs are needed to produce an accurate PDF for 12 input variables. The results prove that the EDR provides the numerical efficiency required for the tail-end probability prediction of manufacturing problems with a large number of input variables, while maintaining high accuracy.

A Probabilistic Estimation Method of Reactive Power Controlled by PCS using Measurement Data of Switchgears with Sensors

SUMMARYIt becomes an important issue to maintain voltage profile within the allowable range in distribution systems with a large number of distributed generators (DGs), such as Photovoltaic (PV). Although reactive power control by power conditioning system (PCS) is effective to mitigate the voltage increase, there is a possibility that active power curtailment is needed to provide enough amount of reactive power due to current limitation. Here, it is expected that the active power curtailment can be avoided if a cooperative voltage control between load ratio tap controller (LRT) and PCS is realized. To this end, system operators have to know the amount of reactive power controlled by PCS. Hence, in this paper, we developed a new state estimation method of the reactive power using the measurement data obtained by the switchgears with sensors. In addition, a probabilistic approach was applied because an accurate estimation is theoretically impossible only by using the limited number of measurement data. The proposed method was tested in a hierarchic distribution system model and its effectiveness was shown through simulations.

Estimating the probability of exceeding the maximum residue limit for Japanese tea using a crop residue model.

Maximum residue limits (MRLs) for pesticides in export countries from Japan often become a trade barrier for Japanese tea. The purpose of this study is to develop a probabilistic risk estimation method for pesticide residues in green tea. First, we developed a model to estimate the pesticide residue level in green tea. Second, we introduced a regression model for pesticide half-lives on plants, one of the most critical parameters in the model. Finally, we estimated the time-course change of the distribution of the residue level by setting the probability distribution to the half-lives on tea leaves. Applying the model to three pesticides, acetamiprid, dinotefuran, and thiamethoxam, we suggested that the pre-harvest interval of thiamethoxam should be increased by three weeks for export to Taiwan. For EU nations, the MRL excess probabilities of acetamiprid and dinotefuran were measured as 99.6% and 99.5%, respectively, even 28 days after spraying.

ESTIMATION OF NATURAL VENTILATION PARAMETERS BY A BAYESIAN APPROACH

Natural ventilation is a good measure to reduce the thermal load of a building and create a good thermal environment for the occupants. To predict the ventilation performance of a building, related ventilation parameters should be estimated. However, obtaining an accurate prediction or estimation before the construction is completed is difficult because of the fluctuating characteristics of turbulent, complex local flow field affected by surrounding buildings and city-scale topology. These problems make the application of natural ventilation difficult, and therefore, an in situ experimental method adopted after the construction of a building is completed would be a more reliable way of obtaining accurate ventilation parameters. The tracer gas method is a frequently used in situ method to examine ventilation performance. However, deterministic evaluation using this method has its drawbacks in terms of accuracy, and the uncertainty of results could be large because of fluctuating environmental conditions and intrinsic error of measurement. Therefore, when parameters related to natural ventilation are estimated, the estimates and their uncertainty should be concurrently evaluated. In this study, using a Bayesian approach, we propose a new estimation method that yields not only the estimates, but also the probability distribution of the estimates. To validate the proposed method, we reproduced a tracer gas experiment using computational fluid dynamics (CFD) simulation because the experimental absolute true values that are unknown in most situations were required for evaluating the proposed method. Using the data from CFD simulation, estimations of the ventilation rate and effective room volume were obtained using two different methods: probabilistic estimation based on Bayes' theorem, and deterministic estimation using the quasi-Newton method. In the probabilistic estimation, a method of simultaneously estimating the degree of the difference between the measurement value and the prediction value required for estimation was proposed. The estimation results showed that the estimated values of the deterministic method and probabilistic method (mode of probability distribution function) were very close to the true value set in the CFD simulation. However, the probabilistic method has some advantages over the deterministic method in that the former method can provide not only the estimates, but also the probability distribution of each estimate and its confidence interval. Additionally, in the case of a simultaneous estimation of more than two unknown parameters, it is important to analyze the correlation among parameters. By creating a joint probability density function, the correlation between the estimation parameters is determined, and this is another important advantage of the probabilistic estimation method.

Power supply risk assessment method for relay protection system faults

Abstract The influence and the potential risk due to hidden faults of a relay protection system on power supply in distribution systems are paid more and more attention to. A probability analysis method is used to analyse fault characteristics and action mechanism of dominant faults, hidden misoperation and non-operation of the relay protection systems, and failure probability model of relay protection system is constructed and simplified. The effects of dominant faults, hidden misoperation and non-operation of the relay protection systems on the reduced power supply load power are analysed, and a probabilistic model for reduced power supply load power is constructed by three parts corresponding to dominant faults, hidden misoperation and non-operation. A probability calculation method of power supply risk occurrence due to hidden faults of relay protecttion system is proposed considering the fault probability of the relay protection systems, the frequency of the hidden faults occurring in operation period, the reduced power supply load power or load power outage, and the connection mode of the in-lines, out-lines and transformers in a substation. The feasibility and applicability of the proposed method for estimation of risk value probability of the relay protection systems is verified by two studied examples.

Enhanced automatic sleep spindle detection: a sliding window-based wavelet analysis and comparison using a proposal assessment method

Sleep spindles are thought to be related to some sleep diseases and play an important role in memory consolidation. They were traditionally identified by physiology experts based on rules and recently detected by automatic algorithms. However, many automatic approaches were validated on the different electroencephalogram (EEG) using various assessment methods, making it difficult to appraised a method objectively and fairly. In this paper, we proposed a sliding window-based probability estimation (SWPE) method for sleep spindle detection. We performed a continuous wavelet transform with Mexican hat wavelet function, following by a sliding window to find out the candidate spindle points corresponding to the large wavelet coefficients at the frequencies of spindles and estimated their probabilities. To enhance the results, we used the envelope of the rectified signal to reject some false sleep spindle candidates. This was an enhanced method and we called it SWPE-E in this paper. Finally, we compared our approaches with four approaches on the same public available EEG database, and the result showed the significative improvement of our proposed approaches.

Modeling the Path-Dependency of the Credit Exposure in Vanilla Products

This paper focuses on modeling the scenario path-dependency of the counterparty credit exposure. The purpose of this paper is threefold: (1) to propose a simple method for consistent estimation of pathwise European swaption exercise probability; (2) to discuss accurate pathwise simulation of barrier option exposure; and (3) to present some exact formulas for the calculation of standalone expected positive exposure and potential future exposure for a single-currency vanilla swap, a physically settled European swaption, and a barrier option without Monte Carlo simulation. These exact formulations are of practical importance to computing standalone exposure profiles, exposure model validation, and system benchmarking.