Probabilistic Monte-Carlo method for modelling and prediction of electronics component life

Power electronics are widely used in electric vehicles, railway locomotive and new generation aircrafts. Reliability of these components directly affect the reliability and performance of these vehicular platforms. In recent years, several research work about reliability, failure mode and aging analysis have been extensively carried out. There is a need for an efficient algorithm able to predict the life of power electronics component. In this paper, a probabilistic Monte-Carlo framework is developed and applied to predict remaining useful life of a component. Probability distributions are used to model the component’s degradation process. The modelling parameters are learned using Maximum Likelihood Estimation. The prognostic is carried out by the mean of simulation in this paper. Monte-Carlo simulation is used to propagate multiple possible degradation paths based on the current health state of the component. The remaining useful life and confident bounds are calculated by estimating mean, median and percentile descriptive statistics of the simulated degradation paths. Results from different probabilistic models are compared and their prognostic performances are evaluated

Parametric, Nonparametric, and Semiparametric Linear Regression in Classical and Bayesian Statistical Quality Control

Statistical process control (SPC) is used in many fields to understand and monitor desired processes, such as manufacturing, public health, and network traffic. SPC is categorized into two phases; in Phase I historical data is used to inform parameter estimates for a statistical model and Phase II implements this statistical model to monitor a live ongoing process. Within both phases, profile monitoring is a method to understand the functional relationship between response and explanatory variables by estimating and tracking its parameters. In profile monitoring, control charts are often used as graphical tools to visually observe process behaviors. We construct a practitioner’s guide to provide a stepby- step application for parametric, nonparametric, and semiparametric methods in profile monitoring, creating an in-depth guideline for novice practitioners. We then consider the commonly used cumulative sum (CUSUM), multivariate CUSUM (mCUSUM), exponentially weighted moving average (EWMA), multivariate EWMA (mEWMA) charts under a Bayesian framework for monitoring respiratory disease related hospitalizations and global suicide rates with parametric, nonparametric, and semiparametric linear models

A Bayesian time-to-event pharmacokinetic model for sequential phase I dose-escalation trials with multiple schedules

Phase I dose-escalation trials constitute the first step in investigating the safety of potentially promising drugs in humans. Conventional methods for phase I dose-escalation trials are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial. Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing dose-escalation trial to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model. In a simulation study, the developed appraoch is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The \texttt{R} and \texttt{Stan} code for the implementation of an illustrative sequential phase I trial example is publicly available at https://github.com/gunhanb/TITEPK_sequential

An Analysis of the Spatio-Temporal Factors Affecting Aircraft Conflicts Based on Simulation Modelling

The demand for air travel worldwide continues to grow at a rapid rate, especially in Europe and the United States. In Europe, the demand exceeded predictions with a real annual growth of 7.1% in the period 1985-1990, against a prediction of 2.4%. By the year 2010, the demand is expected to double from the 1990 level. Within the UK international scheduled passenger traffic is predicted to increase, on average, by 5.8 per cent per year between 1999 and 2003. The demand has not been matched by availability of capacity. In Western Europe many of the largest airports suffer from runway capacity constraints. Europe also suffers from an en-route airspace capacity constraint, which is determined by the workload of the air traffic controllers, i.e. the physical and mental work that controllers must undertake to safely conduct air traffic under their jurisdiction through en-route airspace. The annual cost to Europe due to air traffic inefficiency and congestion in en-route airspace is estimated to be 5 billion US Dollars, primarily due to delays caused by non-optimal route structures and reduced productivity of controllers due to equipment inefficiencies. Therefore, to in order to decrease the total delay, an increase in en-route capacity is of paramount importance. At a global scale and in the early 1980s, the International Civil Aviation Organisation (ICAO) recognised that the traditional air traffic control (ATC) systems would not cope with the growth in demand for capacity. Consequently new technologies and procedures have been proposed to enable ATC to cope with this demand, e.g. satellite-based system concept to meet the future civil aviation requirements for communication, navigation and surveillance/ air traffic management (CNS/ATM). In Europe, the organisation EUROCONTROL (established in 1960 to co-ordinate European ATM) proposed a variety of measures to increase the capacity of en-route airspace. A key change envisaged is the increasing delegation of responsibilities for control to flight crew, by the use of airborne separation assurance between aircraft, leading eventually to ?free flight? airspace. However, there are major concerns regarding the safety of operations in ?free flight? airspace. The safety of such airspace can be investigated by analysing the factors that affect conflict occurrence, i.e. a loss of the prescribed separation between two aircraft in airspace. This paper analyses the factors affecting conflict occurrence in current airspace and future free flight airspace by using a simulation model of air traffic controller workload, the RAMS model. The paper begins with a literature review of the factors that affect conflict occurrence. This is followed by a description of the RAMS model and of its use in this analysis. The airspace simulated is the Mediterranean Free Flight region, and the major attributes of this region and of the traffic demand patterns are outlined next. In particular a day?s air traffic is simulated in the two airspace scenarios, and rules for conflict detection and resolution are carefully defined. The following section outlines the framework for analysing the output from the simulations, using negative binomial (NB) and generalised negative binomial (GNB) regression, and discusses the estimation methods required. The next section presents the results of the regression analysis, taking into account the spatio-temporal nature of the data. The following section presents an analysis of the spatial and temporal pattern of conflicts in the two airspace scenarios across a day, highlighting possible metrics to indicate this. The paper concludes with future research directions based upon this analysis.

A Data-Driven Predictive Model of Reliability Estimation Using State-Space Stochastic Degradation Model

The concept of the Industrial Internet of Things (IIoT) provides the foundation to apply data-driven methodologies. The data-driven predictive models of reliability estimation can become a major tool in increasing the life of assets, lowering capital cost, and reducing operating and maintenance costs. Classical models of reliability assessment mainly rely on lifetime data. Failure data may not be easily obtainable for highly reliable assets. Furthermore, the collected historical lifetime data may not be able to accurately describe the behavior of the asset in a unique application or environment. Therefore, it is not an optimal approach anymore to conduct a reliability estimation based on classical models. Fortunately, most of the industrial assets have performance characteristics whose degradation or decay over the operating time can be related to their reliability estimates. The application of the degradation methods has been recently increasing due to their ability to keep track of the dynamic conditions of the system over time. The main purpose of this study is to develop a data-driven predictive model of reliability assessment based on real-time data using a state-space stochastic degradation model to predict the critical time for initiating maintenance actions in order to enhance the value and prolonging the life of assets. The new degradation model developed in this thesis is introducing a new mapping function for the General Path Model based on series of Gamma Processes degradation models in the state-space environment by considering Poisson distributed weights for each of the Gamma processes. The application of the developed algorithm is illustrated for the distributed electrical systems as a generic use case. A data-driven algorithm is developed in order to estimate the parameters of the new degradation model. Once the estimates of the parameters are available, distribution of the failure time, time-dependent distribution of the degradation, and reliability based on the current estimate of the degradation can be obtained

Simulation of between repeat variability in real time PCR reactions

While many decisions rely on real time quantitative PCR (qPCR) analysis few attempts have hitherto been made to quantify bounds of precision accounting for the various sources of variation involved in the measurement process. Besides influences of more obvious factors such as camera noise and pipetting variation, changing efficiencies within and between reactions affect PCR results to a degree which is not fully recognized. Here, we develop a statistical framework that models measurement error and other sources of variation as they contribute to fluorescence observations during the amplification process and to derived parameter estimates. Evaluation of reproducibility is then based on simulations capable of generating realistic variation patterns. To this end, we start from a relatively simple statistical model for the evolution of efficiency in a single PCR reaction and introduce additional error components, one at a time, to arrive at stochastic data generation capable of simulating the variation patterns witnessed in repeated reactions (technical repeats). Most of the variation in C-q values was adequately captured by the statistical model in terms of foreseen components. To recreate the dispersion of the repeats' plateau levels while keeping the other aspects of the PCR curves within realistic bounds, additional sources of reagent consumption (side reactions) enter into the model. Once an adequate data generating model is available, simulations can serve to evaluate various aspects of PCR under the assumptions of the model and beyond

Aeronautical Engineering: A special bibliography with indexes, supplement 64, December 1975

This bibliography lists 288 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1975

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place

Intelligent grain size profiling using neural network and application to sanding potential prediction in real time.

Production of hydrocarbon from both consolidated and unconsolidated clastic reservoir rocks poses a risk of sand production especially if a well articulated programme of sand management strategy is not put in place to deal with the problem at the onset of field development. A well articulated programme of sand management would include sand production potential prediction in real time if it is going to be effective at all in achieving the goal of dealing with likely sand problem. Sanding potential prediction in real time is considered an element of sand management strategy that involves the evaluation of risk of sand failure/production and the prediction of the likely sand rate and volume to facilitate optimum design of both downhole and surface equipment especially as related to sand control. Sanding potential prediction is therefore very crucial to reducing costs of field developments to make hitherto unattractive development environments profitable. This undoubtedly will impact positively the present drive to increase worldwide production of hydrocarbon . Specifically, real time sanding potential prediction enables timely reservoir management decisions relating to the choice, design and installation of sand control methods. It is also an important input to sand monitoring and topside management. The current sanding potential prediction models in the industry are found to lack the robustness to predict sanding potential in real time. They also are unable to provide the functionality to track the grain size distributions of the sand producing formation and that of the produced sand. This functionality can be useful in the application of grain size distribution to sanding potential prediction. The scope of this work therefore covers the development of coupled models for grain size distribution and sanding potential predictions in real time. A previous work has introduced the use of a commercial neural network technique for grain size distribution prediction. This work has built upon this by using a purposefully coded neural network in conjunction with statistical techniques to develop a model for grain size distribution prediction in both horizontal and vertical directions and extending the application to failure analysis and prediction of strength and sanding potential in formation rocks. The theoretical basis for this work consists in the cross relationships between formation petrophysical properties and grain size distribution parameters on one hand and between grain size distribution parameters and formation strength parameters on the other hand. Hoek and Brown failure criterion, through an analytical treatment, serves as the platform for the development of the failure model, which is coupled to the grain size distribution and Unconfined Compressive Strength (UCS) models. The results obtained in this work have further demonstrated the application of neural network to grain size distribution prediction. They also demonstrate that grain size distribution information can be used in monitoring changes in formation strength and by extension, the formation movement within the failure envelope space especially during production from a reservoir formation