DESIGN AND RELIABILITY ASSESSMENT OF HIGH POWER LED AND LED-BASED SOLID STATE LIGHTING

Lumen depreciation and color quality change of high power LED-based solid state light (SSL) are caused by the combination of various degradation mechanisms. The analytical/experimental models on the system as well as component-level are proposed to analyze the complex reliability issues of the LED-based solid SSL. On the system-level front, a systematic approach to define optimum design domains of LED-based SSL for a given light output requirement is developed first by taking cost, energy consumption and reliability into consideration. Three required data sets (lumen/LED, luminaire efficacy, and L70 lifetime) to define design domains are expressed as contour maps in terms of two most critical operating parameters: the forward current and the junction temperature (If and Tj). Then, the available domain of design solutions is defined as a common area that satisfies all the requirements of a luminaire. Secondly, a physic of failure (PoF) based hierarchical model is proposed to estimate the lifetime of the LED-based SSL. The model is implemented successfully for an LED-based SSL cooled by a synthetic jet, where the lifetime of a prototypical luminaire is predicted from LED lifetime data using the degradation analyses of the synthetic jet and the power electronics. On the component-level front, a mathematical model and an experimental procedure are developed to analyze the degradation mechanisms of high power LEDs. In the approach, the change in the spectral power distribution (SPD) caused by the LED degradation is decomposed into the contributions of individual degradation mechanisms so that the effect of each degradation mechanism on the final LED degradation is quantified. It is accomplished by precise deconvolution of the SPD into the leaked blue light and the phosphor converted light. The model is implemented using the SPDs of a warm white LED with conformally-coated phosphor, obtained before and after 9,000 hours of operation. The analysis quantifies the effect of each degradation mechanism on the final values of lumen, CCT and CRI

Stress-Lifetime Joint Distribution Model for Performance Degradation Failure

The high energy density self-healing metallized film pulse capacitor has been applied to all kinds of laser facilities for their power conditioning systems under several stress levels, such as 23kV, 30kV and 35kV, whose reliability performance and maintenance costs are affected by the reliability of capacitors. Due to the costs and time restriction, how to assess the reliability of highly reliable capacitors under a certain stress level as soon as possible becomes a challenge. Accelerated degradation test provides a way to predict its lifetime and reliability effectively. A model called stress-lifetime joint distribution model and an analysis method based on accelerated degradation data of metallized film pulse capacitors are described in this paper. Also described is a method for estimating the distribution of time to failure. The estimators of the unknown parameters in the model are given respectively. Both the failure probability density function (pdf) and the cumulative distribution function (cdf) can be presented by the model. Based on these estimators and the pdf/cdf, the reliability model of metallized film pulse capacitors is obtained. According to the reliability model, the probability of capacitors under stress 23kV that survive to 20000 shots is presented. Then the Kolmogorov-Smirnov test is performed to validate the model. The result shows that the reliability of capacitors under a certain stress level can be assessed as soon as possible by using the model, once the estimators of the unknown parameters in the model are obtained

Degradation modeling applied to residual lifetime prediction using functional data analysis

Sensor-based degradation signals measure the accumulation of damage of an engineering system using sensor technology. Degradation signals can be used to estimate, for example, the distribution of the remaining life of partially degraded systems and/or their components. In this paper we present a nonparametric degradation modeling framework for making inference on the evolution of degradation signals that are observed sparsely or over short intervals of times. Furthermore, an empirical Bayes approach is used to update the stochastic parameters of the degradation model in real-time using training degradation signals for online monitoring of components operating in the field. The primary application of this Bayesian framework is updating the residual lifetime up to a degradation threshold of partially degraded components. We validate our degradation modeling approach using a real-world crack growth data set as well as a case study of simulated degradation signals.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS448 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Big Data and Reliability Applications: The Complexity Dimension

Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges in big data analytics. Meeker and Hong (2014, Quality Engineering, pp. 102-116) provided an extensive discussion of the opportunities and challenges in big data and reliability, and described engineering systems that can generate big data that can be used in reliability analysis. Meeker and Hong (2014) focused on large scale system operating and environment data (i.e., high-frequency multivariate time series data), and provided examples on how to link such data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. This paper intends to extend that discussion by focusing on how to use data with complicated structures to do reliability analysis. Such data types include high-dimensional sensor data, functional curve data, and image streams. We first provide a review of recent development in those directions, and then we provide a discussion on how analytical methods can be developed to tackle the challenging aspects that arise from the complexity feature of big data in reliability applications. The use of modern statistical methods such as variable selection, functional data analysis, scalar-on-image regression, spatio-temporal data models, and machine learning techniques will also be discussed.Comment: 28 pages, 7 figure

Life Cycle Well Integrity and Reliability

Master's thesis for Petroleum engineeringThis thesis presents a method for reliability based casing design. The method is inspired by the model described by Das et al. (2015) - A model for Well Reliability Analysis throughout the Life of a Well Using Barrier Engineering and Performance (1). The purpose of this model is to show how reliability based casing design, based on statistical data, can be performed in practice. Monte Carlo simulation conducted in MATLAB is the basis of the approach. Statistical data of load and strength simulate a casing burst scenario for a life cycle period of 20 years. Degradation factors associated with casing wear and corrosion are accounted for. Based on the result from this simulation, the underlying life time distribution was identified using Nelson-estimator. Maximum Likelihood estimation was used to calculate parameters for the identified life time distribution. The results from the simulated data showed that the underlying lifetime distribution could be represented by a Weibull distribution. From this distribution, failure rate, failure function and survival function was found. The presented approach show how it is possible to quantify the reliability of a given design. Some of the identified challenges in using a reliability based design approach in practice, is related to the lack of qualitative statistical input data of load, and proper estimation of degradation factors related to the casing strength. To get an overview of the most important aspect of well design an introduction to the regulations, well integrity and load cases are given. The different design approaches used in well design is also explained and compared to get a full overview of how reliability based design differs from other design approaches

A practical degradation based method to predict long-term moisture incursion and colour change in high power LEDs

The effect of relative humidity on LEDs and how the moisture incursion is associated to the color shift is studied. This paper proposes a different approach to describe the lumen degradation of LEDs due to the long-term effects of humidity. Using the lumen degradation data of different types of LEDs under varying conditions of relative humidity, a humidity based degradation model (HBDM) is developed. A practical estimation method from the degradation behaviour is proposed to quantitatively gauge the effect of moisture incursion by means of a humidity index. This index demonstrates a high correlation with the color shift indicated by the LED's yellow to blue output intensity ratio. Physical analyses of the LEDs provide a qualitative validation of the model, which provides good accuracy with longer periods of moisture exposure. The results demonstrate that the HBDM is an effective indicator to predict the extent of the long-term impact of humidity and associated relative color shift

Resilient random modulo cache memories for probabilistically-analyzable real-time systems

Fault tolerance has often been assessed separately in safety-related real-time systems, which may lead to inefficient solutions. Recently, Measurement-Based Probabilistic Timing Analysis (MBPTA) has been proposed to estimate Worst-Case Execution Time (WCET) on high performance hardware. The intrinsic probabilistic nature of MBPTA-commpliant hardware matches perfectly with the random nature of hardware faults. Joint WCET analysis and reliability assessment has been done so far for some MBPTA-compliant designs, but not for the most promising cache design: random modulo. In this paper we perform, for the first time, an assessment of the aging-robustness of random modulo and propose new implementations preserving the key properties of random modulo, a.k.a. low critical path impact, low miss rates and MBPTA compliance, while enhancing reliability in front of aging by achieving a better – yet random – activity distribution across cache sets.Peer ReviewedPostprint (author's final draft

Crater population and resurfacing of the Martian north polar layered deposits

Present-day accumulation in the north polar layered deposits (NPLD) is thought to occur via deposition on the north polar residual cap. Understanding current mass balance in relation to current climate would provide insight into the climatic record of the NPLD. To constrain processes and rates of NPLD resurfacing, a search for craters was conducted using images from the Mars Reconnaissance Orbiter Context Camera. One hundred thirty craters have been identified on the NPLD, 95 of which are located within a region defined to represent recent accumulation. High Resolution Imaging Science Experiment images of craters in this region reveal a morphological sequence of crater degradation that provides a qualitative understanding of processes involved in crater removal. A classification system for these craters was developed based on the amount of apparent degradation and infilling and where possible depth/diameter ratios were determined. The temporal and spatial distribution of crater degradation is interpreted to be close to uniform. Through comparison of the size-frequency distribution of these craters with the expected production function, the craters are interpreted to be an equilibrium population with a crater of diameter D meters having a lifetime of ~30.75D^(1.14) years. Accumulation rates within these craters are estimated at 7.2D^(−0.14) mm/yr, which corresponds to values of ~3–4 mm/yr and are much higher than rates thought to apply to the surrounding flat terrain. The current crater population is estimated to have accumulated in the last ~20 kyr or less