Semi-empirical prediction of moisture build-up in an electronic enclosure using analysis of variance (ANOVA)

Nasirabadi, Parizad Shojaee; Conseil-Gudla, Hélène; Mohanty, Sankhya; Jabbari, Masoud; Ambat, Rajan; Hattel, Jesper Henri

doi:10.1109/eptc.2016.7861588

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…These states are given as and as given in (4). The first step is the conversion of continuous state-space model into a discrete state-state model (9). The MATLAB routines are used to find matrices F, G, and C in discrete state-space model (10).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The package also uses ANOVA to provide the necessary analysis. An ANOVA based prediction of moisture buildup in electronic enclosure is proposed in [9]. The prediction of dataset by software engineers using ANOVA is demonstrated in [10].…”

Section: Anova Predictor Designmentioning

confidence: 99%

“…A relatively smaller sampling rate results in large number of samples, and thus gives more accurate replica of the continuous state-space model. The general form of discrete state-space model is given by, (9) (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No.…”

Section: B State Space Model In Discrete Timementioning

confidence: 99%

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A Comparison of Predictive Parameter Estimation using Kalman Filter and Analysis of Variance

Khan¹,

Mehdi²,

Saleem³

et al. 2017

ijacsa

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Abstract-The design of a controller significantly improves if internal states of a dynamic control system are predicted. This paper compares the prediction of system states using Kalman filter and a novel approach analysis of variance (ANOVA). Kalman filter has been successfully applied in several applications. A significant advantage of Kalman filter is its ability to use system output to predict the future states. It has been observed that Kalman filter based predictive controller design outperforms many other approaches. An important drawback of such controllers is however that their performances deteriorate in situations where the system states have no correlation with the output. This paper takes a hypothetical model of a helicopter and builds system model using the statespace diagram. The design is implemented using SIMULINK. It has been observed that in situations where the states are dependent on system output, the ANOVA based state prediction gives comparable results with that of Kalman filter based parameter estimation. The ANOVA based parameter prediction, however outperforms Kalman filter based parameter prediction in situations where the output does not directly contribute in a particular state. The research was based on empirical results. Rigorous testing was performed on four internal states to prove that ANOVA based predictive parameter estimation technique outperforms Kalman based parameter estimation in situations where the system internal states is not directly linked with the output.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Anova Predictor Designmentioning

confidence: 99%

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A Comparison of Predictive Parameter Estimation using Kalman Filter and Analysis of Variance

Khan¹,

Mehdi²,

Saleem³

et al. 2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…A statistical model is developed by ANOVA of the numerical data in a full factorial design framework [35][36][37][38][39]. ANOVA is primarily designed for the analysis of experimental data.…”

Section: Statistical Analysis Of Design Variablesmentioning

confidence: 99%

“…For an adequate model R 2 should be close to 1 [50]. In general for a well fitted model R 2 value should be above 0.9 [35]. Other important outputs from ANOVA are F-values (Fisher variation ratio) and p-values (probability value) [36].…”

Section: Analysis Of Variancementioning

confidence: 99%

Electrochemical-Thermal Modelling and Optimisation of Lithium-Ion Battery Design Parameters Using Analysis of Variance

2017

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A 1D electrochemical-thermal model of an electrode pair of a lithium ion battery is developed in Comsol Multiphysics. The mathematical model is validated against the literature data for a 10 Ah lithium phosphate (LFP) pouch cell operating under 1 C to 5 C electrical load at 25 • C ambient temperature. The validated model is used to conduct statistical analysis of the most influential parameters that dictate cell performance; i.e., particle radius (r p ); electrode thickness (L pos ); volume fraction of the active material (ε s,pos ) and C-rate; and their interaction on the two main responses; namely; specific energy and specific power. To achieve an optimised window for energy and power within the defined range of design variables; the range of variation of the variables is determined based on literature data and includes: r p : 30-100 nm; L pos : 20-100 µm; ε s,pos : 0.3-0.7; C-rate: 1-5. By investigating the main effect and the interaction effect of the design variables on energy and power; it is observed that the optimum energy can be achieved when (r p < 40 nm); (75 µm < L pos < 100 µm); (0.4 < ε s,pos < 0.6) and while the C-rate is below 4C. Conversely; the optimum power is achieved for a thin electrode (L pos < 30 µm); with high porosity and high C-rate (5 C).

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Humidity build-up in electronic enclosures exposed to different geographical locations by RC modelling and reliability prediction

Conseil-Gudla

Staliulionis

Mohanty

et al. 2018

Microelectronics Reliability

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Electronic devices are exposed to a wide range of climatic conditions. This study shows a reliability prediction of electronic devices exposed to different climates (from arid to humid and cold to hot regions). Temperature and humidity probability distribution functions have been calculated to indicate the change of climate exposure along year. While temperature and relative humidity (RH) are important factors in terms of water diffusion and electronic reliability, the internal climatic condition of 25 ˚C and 60% RH is widely used as threshold for electronic safety. Acceleration factors according to this steady state (25 ˚C and 60% RH) have been calculated for the different climates, and the protection offered by the enclosures has been estimated under different casing materials and resistor-capacitor (RC) simulation. This method offers a way to predict the average value of failure rate for electronic devices based on climate information and enclosure material.

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Semi-empirical prediction of moisture build-up in an electronic enclosure using analysis of variance (ANOVA)

Cited by 6 publications

References 18 publications

A Comparison of Predictive Parameter Estimation using Kalman Filter and Analysis of Variance

A Comparison of Predictive Parameter Estimation using Kalman Filter and Analysis of Variance

Electrochemical-Thermal Modelling and Optimisation of Lithium-Ion Battery Design Parameters Using Analysis of Variance

Humidity build-up in electronic enclosures exposed to different geographical locations by RC modelling and reliability prediction

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