Statistical and intelligent reliability analysis of multi-layer ceramic capacitor for ground mobile applications using Taguchi’s approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Cherry Bhargava ◽  
Pardeep Kumar Sharma
Keyword(s):  
Regression Analysis ◽  
Electronic Device ◽  
Back Propagation ◽  
Empirical Method ◽  
Residual Lifetime ◽  
Content Type ◽  
Feed Forward Back Propagation ◽  
Intelligent Technique ◽  
Global Issue ◽  
Faulty Component

PurposeAlthough Multi-Layer Ceramic Capacitors (MLCC) are known for its better frequency performance and voltage handling capacity, but under various environmental conditions, its reliability becomes a challenging issue. In modern era of integration, the failure of one component can degrade or shutdown the whole electronic device. The lifetime estimation of MLCC can enhance the reuse capability and furthermore, reduces the e-waste, which is a global issue.Design/methodology/approachThe residual lifetime of MLCC is estimated using empirical method i.e. Military handbook MILHDBK2017F, statistical method i.e. regression analysis using Minitab18.1 as well as intelligent technique i.e. artificial neural networks (ANN) using MATLAB2017b. ANN Feed-Forward Back-Propagation learning with sigmoid transfer function [3–10–1–1] is considered using 73% of available data for training and 27% for testing and validation. The design of experiments is framed using Taguchi’s approach L16 orthogonal array.FindingsAfter exploring the lifetime of MLCC, using empirical, statistical and intelligent techniques, an error analysis is conducted, which shows that regression analysis has 97.05% accuracy and ANN has 94.07% accuracy.Originality/valueAn intelligent method is presented for condition monitoring and health prognostics of MLCC, which warns the user about its residual lifetime so that faulty component can be replaced in time.

Lifetime estimation of tantalum capacitor for mobile applications using empirical and experimental techniques: a DOE approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Cherry Bhargava ◽  
Pardeep Kumar Sharma ◽  
Ketan Kotecha
Keyword(s):  
Mobile Applications ◽  
Empirical Method ◽  
Circuit Board ◽  
Experimental Techniques ◽  
Residual Lifetime ◽  
Content Type ◽  
Accelerated Life ◽  
Tantalum Capacitor ◽  
Faulty Component ◽  
The Military

PurposeCapacitors are one of the most common passive components on a circuit board. From a tiny toy to substantial satellite, a capacitor plays an important role. Untimely failure of a capacitor can destruct the entire system. This research paper targets the reliability assessment of tantalum capacitor, to reduce e-waste and enhance its reusable capability.Design/methodology/approachThe residual lifetime of a tantalum capacitor is estimated using the empirical method, i.e. military handbook MILHDBK2017F, and validated using an experimental approach, i.e. accelerated life testing (ALT). The various influencing acceleration factors are explored, and experiments are designed using Taguchi's approach. Empirical methods such as the military handbook is used for assessing the reliability of a tantalum capacitor, for ground and mobile applications.FindingsAfter exploring the lifetime of a tantalum capacitor using empirical and experimental techniques, an error analysis is conducted, which shows the validity of empirical technique, with an accuracy of 95.21%.Originality/valueThe condition monitoring and health prognostics of tantalum capacitors, for ground and mobile applications, are explored using empirical and experimental techniques, which warns the user about its residual lifetime so that the faulty component can be replaced in time.

Aircraft turbines time-to-failures process modeling using RBF NN

2019 ◽  
Vol 26 (2) ◽  
pp. 249-259
Author(s):  
Ahmed Z. Al-Garni ◽  
Wael G. Abdelrahman ◽  
Ayman M. Abdallah
Keyword(s):  
Back Propagation ◽  
Computation Time ◽  
Aircraft Engine ◽  
Computational Time ◽  
Time To Failure ◽  
Content Type ◽  
Weibull Regression ◽  
Failure Data ◽  
Feed Forward Back Propagation ◽  
Accuracy Of Results

Purpose The purpose of this paper is to formulate a specialized artificial neural network algorithm utilizing radial basis function (RBF) for modeling of time to failure of aircraft engine turbines. Design/methodology/approach The model uses training failure data collected from operators of turboprop aircraft working in harsh desert conditions where sand erosion is a detrimental factor in reducing turbine life. Accordingly, the model is more suited to accurate prediction of life of critical components of such engines. The used RBF employs a closest neighbor type of classifier and the hidden unit’s activation is based on the displacement between the early prototype and the input vector. Findings The results of the algorithm are compared to earlier work utilizing Weibull regression modeling, as well as Feed Forward Back Propagation NN. The results show that the failure rates estimated by RBF more closely match actual failure data than the estimations by both other models. The trained model showed reasonable accuracy in predicting future failure events. Moreover, the technique is shown to have comparatively higher efficiency even with reduced number of neurons in each layer of ANN. This significantly decreases computation time with minimum effect on the accuracy of results. Originality/value Using RBF technique significantly decreases the computational time with minimum effect on the accuracy of results.

Investigations on Drilling of Multimaterial and Analysis by ANN

2010 ◽  
Vol 443 ◽  
pp. 347-352 ◽  
Author(s):  
Vijayan Krishnaraj ◽  
Redouane Zitoune ◽  
Francis Collombet
Keyword(s):  
Regression Analysis ◽  
Back Propagation ◽  
Analytical Investigation ◽  
Feed Forward Back Propagation ◽  
Reinforced Plastic ◽  
Drill Diameter ◽  
Full Factorial Experimental Design ◽  
Carbon Fibre Reinforced Plastic ◽  
Artificial Neural Network Ann ◽  
Propagation Network

This paper presents experimental and analytical investigation on drilling of carbon fibre reinforced plastic and aluminium stacks. The experimental results conducted as per full factorial experimental design reveal that drill diameter and feed rate have significant effects in reducing thrust force and torque while spindle speed has the least effect. The analytical study is based on artificial neural network (ANN) training using feed-forward back propagation network. The correlations obtained by multi-variable regression analysis and ANN, indicate that ANN is more effective than regression analysis.

Stream Flow Modeling of River Swat Using Regression and Artificial Neural Networks (ANNs) Techniques

2011 ◽  
Vol 255-260 ◽  
pp. 679-683 ◽  
Author(s):  
Suleman Daud ◽  
Khan Shahzada ◽  
M. Tufail ◽  
M. Fahad
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Stream Flow ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Flow Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Intelligent Model

This paper presents the utility of Artificial Neural Networks and Regression analysis for the stream flow modeling in Swat River at five discharge gauge station. As an appropriate intelligent model is identified, Artificial Neural Networks (ANNs) is evaluated by comparing it to regression analysis and the available field data. ANNs namely feed forward back propagation neural network (FFBPNN) and regression analysis are introduced and implemented. The research study successfully compared the performance of the ANN and regression models that validated the utility of the two modeling techniques as effective applications to stream flow forecasting.

Prediction of Surface Roughness in Magneto Rheological Abrasive Flow Finishing Process by Artificial Neural Networks and Regression Analysis

2015 ◽  
Vol 766-767 ◽  
pp. 1076-1084
Author(s):  
S. Kathiresan ◽  
K. Hariharan ◽  
B. Mohan
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Regression Analysis ◽  
Back Propagation ◽  
Hydraulic Pressure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Magneto Rheological ◽  
Hidden Neurons ◽  
Abrasive Flow Finishing

In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model.Taguchi’s technique has been used for designing the experiments in order to observe the different values of surface roughness . A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) andF-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.Key words: MRAFF, ANN, Regression analysis

AN ARTIFICIAL INTELLIGENT APPROACH TO TRAFFIC ACCIDENT ESTIMATION: MODEL DEVELOPMENT AND APPLICATION

Transport ◽  
2009 ◽  
Vol 24 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Ali Payıdar Akgüngör ◽  
Erdem Doğan
Keyword(s):  
Genetic Algorithm ◽  
Model Development ◽  
Back Propagation ◽  
Linear Functions ◽  
Model Parameters ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
Average Growth ◽  
Feed Forward Back Propagation

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

Quantitative analysis of the impact of craft worker availability on construction project safety performance

2016 ◽  
Vol 16 (3) ◽  
pp. 307-322 ◽  
Author(s):  
Hossein Karimi ◽  
Timothy R.B. Taylor ◽  
Paul M. Goodrum ◽  
Cidambi Srinivasan
Keyword(s):  
Regression Analysis ◽  
Poisson Regression ◽  
Construction Projects ◽  
Construction Project ◽  
Safety Performance ◽  
Content Type ◽  
Poisson Regression Analysis ◽  
Incident Cases ◽  
The Impact ◽  
Craft Worker

Purpose This paper aims to quantify the impact of craft worker shortage on construction project safety performance. Design/methodology/approach A database of 50 North American construction projects completed between 2001 and 2014 was compiled by taking information from a research project survey and the Construction Industry Institute Benchmarking and Metrics Database. The t-test and Mann-Whitney test were used to determine whether there was a significant difference in construction project safety performance on projects with craft worker recruiting difficulty. Poisson regression analysis was then used to examine the relationship between craft worker recruiting difficulty and Occupational Safety and Health Administration Total Number of Recordable Incident Cases per 200,000 Actual Direct Work Hours (TRIR) on construction projects. Findings The result showed that the TRIR distribution of a group of projects that reported craft worker recruiting difficulty tended to be higher than the TRIR distribution of a group of projects with no craft worker recruiting difficulty (p-value = 0.004). Moreover, the average TRIR of the projects that reported craft worker recruiting difficulty was more than two times the average TRIR of projects that experienced no craft recruiting difficulty (p-value = 0.035). Furthermore, the Poisson regression analysis demonstrated that there was a positive exponential relationship between craft worker recruiting difficulty and TRIR in construction projects (p-value = 0.004). Research limitations/implications The projects used to construct the database are heavily weighted towards industrial construction. Practical implications There have been significant long-term gains in construction safety within the USA. However, if recent craft shortages continue, the quantitative analyses presented herein indicate a strong possibility that more safety incidents will occur unless the shortages are reversed. Innovative construction means and methods should be developed and adopted to work in a safe manner with a less qualified workforce. Originality/value The Poisson regression model is the first model that quantifiably links project craft worker availability to construction project safety performance.

Evaluation of new technologies and materials for printed circuit boards with improved heat dissipation properties

Circuit World ◽  
2016 ◽  
Vol 42 (1) ◽  
pp. 32-36 ◽  
Author(s):  
Michal Baszynski ◽  
Edward Ramotowski ◽  
Dariusz Ostaszewski ◽  
Tomasz Klej ◽  
Mariusz Wojcik ◽  
...  
Keyword(s):  
Thermal Properties ◽  
Printed Circuit Boards ◽  
Heat Dissipation ◽  
Printed Circuit Board ◽  
New Technologies ◽  
Electronic Device ◽  
Circuit Board ◽  
New Materials ◽  
Content Type ◽  
Printed Circuit

Purpose – The purpose of this paper is to evaluate thermal properties of printed circuit board (PCB) made with use of new materials and technologies. Design/methodology/approach – Four PCBs with the same layout but made with use of different materials and technologies have been investigated using thermal camera to compare their thermal properties. Findings – The results show how important the thermal properties of PCBs are for providing effective heat dissipation, and how a simple alteration to the design can help to improve the thermal performance of electronic device. Proper layout, new materials and technologies of PCB manufacturing can significantly reduce the temperature of electronic components resulting in higher reliability of electronic and power electronic devices. Originality/value – This paper shows the advantages of new technologies and materials in PCB thermal management.

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