Sustainability Analysis of a ZnO-NaCl-Based Capacitor Using Accelerated Life Testing and an Intelligent Modeling Approach

From small toys to satellites, capacitors play a vital role as an energy storage element, filtering or controlling other critical tasks. This research paper focuses on estimating the remaining useful life of a nanocomposite-based fabricated capacitor using various experimental and artificial intelligence techniques. Accelerated life testing is used to explore the sustainability and remaining useful life of the fabricated capacitor. The acceleration factors affecting the health of capacitors are investigated, and experiments are designed using Taguchi’s approach. The remaining useful lifetime of the fabricated capacitor is calculated using a statistical technique, i.e., regression analysis using Minitab 18.1 software. An expert model is designed using artificial neural networks (ANN), which warns the user of any upcoming faults and failures. The average remaining useful life of the fabricated capacitor, using accelerated life testing, regression, and artificial neural network, is reported as 13,724.3 h, 14,515.9 h, and 14,247.1 h, respectively. A comparison analysis is conducted, and performance metrics are analyzed to opt for the most efficient technique for the prediction of the remaining useful life of the fabricated capacitor, which confirms 93.83% accuracy using the statistical method and 95.82% accuracy using artificial neural networks. The root mean square error (RMSE) of regression and artificial neural networks is found to be 0.102 and 0.167, respectively, which validates the consistency of the reliability methods.

[1]  Lokavarapu Bhaskara Rao,et al.  Accelerated life testing of nano ceramic capacitors and capacitor test boards using non-parametric method , 2016 .

[2]  Zhongya Zhang,et al.  Artificial neural networks applied to polymer composites: a review , 2003 .

[3]  Meng Zhao,et al.  Reliability issues of lead-free solder joints in electronic devices , 2019, Science and technology of advanced materials.

[4]  Charles S. Whitman,et al.  Impact of ambient temperature set point deviation on Arrhenius estimates , 2012, Microelectron. Reliab..

[5]  Ajit Kumar Kolar,et al.  ANN-GA based optimization of a high ash coal-fired supercritical power plant , 2011 .

[6]  Yaduvir Singh,et al.  An intelligent prognostic model for electrolytic capacitors health monitoring: A design of experiments approach , 2018 .

[7]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[8]  Manoj Kumar Tiwari,et al.  Multiple response optimization using Taguchi methodology and neuro‐fuzzy based model , 2006 .

[9]  P. Kurzweil,et al.  Accelerated Life Testing of Double‐Layer Capacitors: Reliability and Safety under Excess Voltage and Temperature , 2015 .

[10]  H. Morkoç,et al.  A COMPREHENSIVE REVIEW OF ZNO MATERIALS AND DEVICES , 2005 .

[11]  Arthur Jones,et al.  An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks , 2020, Composites Part B: Engineering.

[12]  Sahil Shah,et al.  Predicting stock market index using fusion of machine learning techniques , 2015, Expert Syst. Appl..

[13]  Yaduvir Singh,et al.  Condition monitoring of aluminium electrolytic capacitors using accelerated life testing , 2018, International Journal of Quality & Reliability Management.

[14]  Canras Batunlu,et al.  Reliability Assessment of IGBT Through Modelling and Experimental Testing , 2020, IEEE Access.

[15]  Zhigang Tian,et al.  An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring , 2012, J. Intell. Manuf..

[16]  Gerald E Loeb,et al.  Accelerated life-test methods and results for implantable electronic devices with adhesive encapsulation , 2017, Biomedical microdevices.

[17]  H. Kaebernick,et al.  Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks , 2007 .

[18]  Kaj Nyström,et al.  A unified deep artificial neural network approach to partial differential equations in complex geometries , 2017, Neurocomputing.

[19]  Subrato Bharati,et al.  Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review , 2020, ArXiv.

[20]  G. Reynolds,et al.  Sputtered Modified Barium Titanate for Thin-Film Capacitor Applications , 2012, Materials.

[21]  Du Zhang,et al.  Diagnosis of Component Failures in the Space Shuttlemain Engines Using Bayesian Belief Network: A Feasibility Study , 2003, Int. J. Artif. Intell. Tools.

[22]  Frede Blaabjerg,et al.  Review of Health Prognostics and Condition Monitoring of Electronic Components , 2020, IEEE Access.

[23]  Payal Dande,et al.  Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review. , 2018, Tuberculosis.

[24]  C. Bhargava,et al.  Fabrication of DSSC using doctor blades method incorporating polymer electrolytes , 2021, Materials Research Express.

[25]  Lingli Cui,et al.  An Enhanced Intelligent Diagnosis Method Based on Multi-Sensor Image Fusion via Improved Deep Learning Network , 2020, IEEE Transactions on Instrumentation and Measurement.

[26]  Y. Singh,et al.  Reliability Comparison of a Fabricated Humidity Sensor using Various Artificial Intelligence Techniques , 2017 .

[27]  Dongqing Wei,et al.  Artificial Neural Networks for Prediction of Tuberculosis Disease , 2019, Front. Microbiol..

[28]  Reliability analysis and condition monitoring of polymer based dye sensitized solar cell: a DOE approach , 2021, Materials Research Express.

[29]  Che Hassan Che Haron,et al.  Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models , 2013 .

[30]  K. Mohandas,et al.  Prediction of cutting tool life based on Taguchi approach with fuzzy logic and support vector regression techniques , 2015 .

[31]  M. Lefik.  Some aspects of application of artificial neural network for numerical modeling in civil engineering , 2013 .

[32]  Vicenç Puig,et al.  Leak diagnosis in pipelines using a combined artificial neural network approach , 2021, Control Engineering Practice.

[33]  Lasse Rosendahl,et al.  Methods to improve prediction performance of ANN models , 2003, Simul. Model. Pract. Theory.

[34]  Rahee Walambe,et al.  Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study , 2021, Computers in Human Behavior.

[35]  D. Dinakaran,et al.  Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool , 2017 .

[36]  Bhargava,et al.  Mathematical Modelling and Residual Life Prediction of an Aluminium Electrolytic Capacitor , 2022 .

[37]  Sahil Shah,et al.  Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques , 2015, Expert Syst. Appl..

[38]  Vallayil N. A. Naikan,et al.  Accelerated temperature and voltage life tests on aluminium electrolytic capacitors , 2016 .

[39]  Priti Srinivas Sajja.  Examples and Applications on Artificial Neural Networks , 2021 .

[40]  Serkan Aydin,et al.  An improved approach to the solution of inverse kinematics problems for robot manipulators , 2000 .

[41]  Romit Kulkarni,et al.  Reliability Study of Electronic Components on Board-Level Packages Encapsulated by Thermoset Injection Molding , 2020 .

[42]  Omesh K. Chopra,et al.  Using artificial neural networks to predict the fatigue life of carbon and low-alloy steels , 2000 .

[43]  Yvan Avenas,et al.  Reliability Assessment of Multistate Degraded Systems: An Application to Power Electronic Systems , 2020, IEEE Transactions on Power Electronics.