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

Pardeep Kumar Sharma, Cherry Bhargava and Ketan Kotecha
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Pardeep Kumar Sharma: Stratjuris Partners, Westport, Baner, Pune 411045, India
Cherry Bhargava: Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, India
Ketan Kotecha: Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Lavale, Pune 412115, India

Sustainability, 2021, vol. 13, issue 19, 1-16

Abstract: 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.

Keywords: artificial intelligence techniques; accelerated life testing; artificial neural network; regression analysis; remaining useful life; sustainability; Taguchi’s approach (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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