An intelligent prognostic model for electrolytic capacitors health monitoring: A design of experiments approach

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%.

Validation of diabetes-related awareness instrument (DRAI) to measure diabetics awareness towards risk factors and prevention strategies

“…EFA was run on data collected from 112 diabetic respondents. A sample adequacy was estimated using “ Kaiser – Meyer– Olkin ” (KMO) measure (Bhargava et al. , 2018a, b; Nakic Rados et al.…”

“…Internal consistency estimates item-total correlation and Cronbach's alpha with the threshold value ranging from 0.70 to 0.90 as suggested by the literature (Talmage and Rasher, 1981; Zhang et al. , 2000; Kline, 2013; Bhargava et al. , 2018a, b; Jaggi and Nim, 2020).…”

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

“…AI-based monitoring techniques are utilized for capacitors health estimation. An ANN-based regression technique is incorporated for RUL detection of electrolytic capacitors [149]. Decision regarding health prognostics is determined through a fuzzy-based system.…”

Prediction of Capacitor’s Accelerated Aging Based on Advanced Measurements and Deep Neural Network Techniques

“…ANNs (Artificial Neural Networks) have been applied to predict the remaining useful life of capacitors. For example, a residual lifetime prediction is developed using linear regression and ANNs in [21]. Six factors are considered: temperature, ripple current, humidity, applied voltage, ESR and vibration.…”