Entropy characterisation of overstressed capacitors for lifetime prediction

We propose a method to monitor the ageing and damage of capacitors based on their irreversible entropy generation rate. We overstressed several electrolytic capacitors in the range of 33 µF–100 µF and monitored their entropy generation rate View the MathML source(t ). We found a strong relationship between capacitor degradation and View the MathML source(t ). Therefore, we proposed a threshold for View the MathML source(t ) as an indicator of capacitor time-to-failure. This magnitude is related to both capacitor parameters and to a damage indicator such as entropy. Our method goes beyond the typical statistical laws for lifetime prediction provided by manufacturers. We validated the model as a function of capacitance, geometry, and rated voltage. Moreover, we identified different failure modes, such as heating, electrolyte dry-up and gasification from the dependence of View the MathML source(T) with temperature, T. Our method was implemented in cheap electrolytic capacitors but can be easily applied to any type of capacitor, supercapacitor, battery, or fuel cell.Peer ReviewedPostprint (author's final draft

Earth orbital lifetime prediction model and program

Model definitions and Fortran language to predict earth satellite orbital lifetim

Atmospheric drag model calibrations for spacecraft lifetime prediction

Although solar activity prediction uncertainty normally dominates decay prediction error budget for near-Earth spacecraft, the effect of drag force modeling errors for given levels of solar activity needs to be considered. Two atmospheric density models, the modified Harris-Priester model and the Jacchia-Roberts model, to reproduce the decay histories of the Solar Mesosphere Explorer (SME) and Solar Maximum Mission (SMM) spacecraft in the 490- to 540-kilometer altitude range were analyzed. Historical solar activity data were used in the input to the density computations. For each spacecraft and atmospheric model, a drag scaling adjustment factor was determined for a high-solar-activity year, such that the observed annual decay in the mean semimajor axis was reproduced by an averaged variation-of-parameters (VOP) orbit propagation. The SME (SMM) calibration was performed using calendar year 1983 (1982). The resulting calibration factors differ by 20 to 40 percent from the predictions of the prelaunch ballistic coefficients. The orbit propagations for each spacecraft were extended to the middle of 1988 using the calibrated drag models. For the Jaccia-Roberts density model, the observed decay in the mean semimajor axis of SME (SMM) over the 4.5-year (5.5-year) predictive period was reproduced to within 1.5 (4.4) percent. The corresponding figure for the Harris-Priester model was 8.6 (20.6) percent. Detailed results and conclusions regarding the importance of accurate drag force modeling for lifetime predictions are presented

A comparison of fatigue lifetime prediction models applied to variable amplitude loading

The loads imposed on e.g. offshore structures can vary considerably with time. Lifetime prediction methodologies need to consider possible acceleration and retardation of the crack growth rate due to load sequences. Models based on a linear accumulation of damage will have a limited accuracy and are not considered as a valuable asset in lifetime prediction of structures subjected to variable amplitude loading. This necessitates more complex nonlinear damage evolution models that can be applied in a so-called cycle-by-cycle analysis. In this paper, a comparison is made between three cumulative damage models (Miner, modified Miner and weighted average) and two yield zone models (Wheeler and Willenborg). Experimental data of fatigue crack growth in offshore steel subjected to sequential loading is used as basis of the comparison. The modified Miner model is the most promising of the cumulative damage models but the determination of the parameter α requires laboratory tests. Evaluation of the effects of variation in the model input parameters on estimated lifetime reveals a large influence for the Miner and weighted average approaches

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

Thermal Characterization and Lifetime Prediction of LED Boards for SSL Lamp

This work presents a detailed 3-D thermo-mechanical modelling of two LED board technologies to compare their performance. LED board are considered to be used in high power 800 lumen retrofit SSL (Solid State Lighting) lamp. Thermal, mechanical and life time properties are evaluated by numerical modelling. Experimental results measured on fabricated LED board samples are compared to calculated data. Main role of LED board in SSL lamp is to transport heat from LED die to a heat sink and keep the thermal stresses in all layers as low as possible. The work focuses on improving of new LED board thermal management. Moreover, reliability and lifetime of LED board has been inspected by numerical calculation and validated by experiment. Thermally induced stress has been studied for wide temperature range that can affect the LED boards (-40 to +125°C). Numerical modelling of thermal performance, thermal stress distribution and lifetime has been carried out with ANSYS structural analysis where temperature dependent stress-strain material properties have been taken into account. The objective of this study is to improve not only the thermal performance of new LED board, but also identification of potential problems from mechanical fatigue point of view. Accelerated lifetime testing (e.g., mechanical) is carried out in order to study the failure behaviour of current and newly developed LED board

SSME lifetime prediction and verification, integrating environments, structures, materials: The challenge

The planned missions for the space shuttle dictated a unique and technology-extending rocket engine. The high specific impulse requirements in conjunction with a 55-mission lifetime, plus volume and weight constraints, produced unique structural design, manufacturing, and verification requirements. Operations from Earth to orbit produce severe dynamic environments, which couple with the extreme pressure and thermal environments associated with the high performance, creating large low cycle loads and high alternating stresses above endurance limit which result in high sensitivity to alternating stresses. Combining all of these effects resulted in the requirements for exotic materials, which are more susceptible to manufacturing problems, and the use of an all-welded structure. The challenge of integrating environments, dynamics, structures, and materials into a verified SSME structure is discussed. The verification program and developmental flight results are included. The first six shuttle flights had engine performance as predicted with no failures. The engine system has met the basic design challenges

Lifetime prediction for power converters

Renewable energy is developing rapidly and gaining more and more commercial viability. High reliability of the generation system is essential to maximize the output power. The power inverter is an important unit in this system and is believed to be one of the most unreliable parts. In the case of wind power generation, especially in off-shore wind, when the system reliability requirement is high, a technique to predict the inverter lifetime is invaluable as it would help the inverter designer optimize his design for minimal maintenance. Previous researchers studying inverter lifetime prediction, focus either at device level such as device fatigue damage models, or at system level which require experimental data for their selected device. This work presents a new method to estimate the inverter lifetime from a given mission profile within a reasonable simulation time. Such model can be used as a converter design tool or an on-line lifetime estimation tool after being configured to a real converter system. The key contribution of this work is to link the physics of the power devices to a large scale system simulation within a reasonable framework of time. With this technique, the system down time can be reduced and therefore more power can be generated. Also, the failure damage to the system is avoided which reduces the maintenance cost. A power cycling test is designed to gather the lifetime data of a selected IGBT module. Die-attach solder fatigue is found out to be the dominant failure mode of this IGBT module. The accuracy of widely accepted Miner’s rule, which accumulates damage linearly, is discussed and a nonlinear accumulation method is promoted to predict the lifetime of power inverters

Screw lifetime prediction based on wavelet neural network and empirical mode decomposition

To predict residual lifetime of ball screw, screw lifetime prediction technology based on wavelet neural network (WNN) and empirical mode decomposition (EMD) is proposed. Screw accelerated lifetime test platform is introduced. Accelerometers are installed to monitor ball screw lifetime. With the method of principal component analysis (PCA), high dimension features are mapped to low dimensional space and stored into sample library together with screw expected remaining lifetime. Training samples and testing samples are randomly selected from the sample library to train and test the WNN. Then EMD is used to extract output tendency of WNN. Finally, screw lifetime prediction model can be obtained. The experimental results show that the maximum error of the training samples is 602 hours while the maximum error of the testing samples is 652 hours, which meet the need of screw lifetime prediction