An intelligent prognostic model for electrolytic capacitors health monitoring: A design of experiments approach. (July 2018)

• Record Type: Journal Article
• Authors: Bhargava, Cherry; Banga, Vijay Kumar; Singh, Yaduvir
• Is Part Of: Advances in mechanical engineering. Volume 10:Number 7(2018)
• Publication Date: 2018-07
• Subjects: Residual life -- electrolytic capacitor -- accelerated life testing -- design of experiments -- artificial neural network -- regression; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814018781170
• Languages: English
• ISSNs: 1687-8132

Abstract

In the world of fast-growing technology, the electronic gadgets become obsolete with the invention of advanced technology. Reuse of electronic components is a philosophy now being applied to all manufacturing industries to achieve the goal of reuse technology. The accurate assessment of residual life is of great significance for reuse as well as the successful operation of the application. The prediction of failure before it occurs will, in turn, reduce the repairing cost and strengthen the reputation of the manufacturer in real-time market. This article reports a novel technique to explore the residual life of electrolytic capacitor and validates it, using accelerated life testing. The optimization and evaluation of proposed technology are accomplished using design of experiments methodology, that is, Taguchi's approach to designing the experiments. Prediction of residual life of capacitor is done using regression and artificial neural networks technique. A decision support system is prepared using fuzzy logic, which monitors the current health status of the capacitor and directs the user accordingly. Using six environmental stress and electrical parameters, the actual lifetime of the electrolytic capacitor is accessed, which has been proven as a valid and accurate technique, exhibiting error rate of 2.99%.