To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' visa -vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted. INDEX TERMS Reliability methods, condition monitoring, faults and failures, prognostics, diagnostics.
Purpose An electrolytic capacitor is extensively used as filtering devices in various power supplies and audio amplifiers. Low cost and higher value of capacitance make it more well known. As environmental stress and electrical parameters increase, capacitors degrade on accelerated pace. The paper aims to discuss these issues. Design/methodology/approach This paper focusses on the impact of thermal stress on electrolytic capacitors using accelerated life testing technique. The failure time was calculated based on the change in capacitance, equivalent series resistance and weight loss. The experimental results are compared with the outcome of already available life monitoring methods, and the accuracy level of these methods is accessed. Findings The results of all the three methods are having maximum 55 per cent accuracy. To enhance the accuracy level of theoretical methods, modifications have been suggested. A new method has been proposed, whose outcome is 92 per cent accurate with respect to experimentally obtained outcomes. Practical implications To assess the capacitor’s reliability using an experimental and modified theoretical method, failure prediction can be done before it actually fails. Originality/value A new method has been proposed to access the lifetime of capacitor.
Driving at night with traditional headlamps poses great threats with a large number of accidents occurring during the night because of temporary blindness caused by the headlights of the oncoming traffic. The headlights when in high beam cause temporary visual impairment of human eyes called the Troxler effect. While it reduces the time to react, it also leads to decreased visibility which contributes to the majority of mishaps that occur at night. Customarily the headlight adjustments are controlled manually where poor driving skills or error in judgment can have catastrophic effects. Accidents also occur due to poor lighting conditions as the current regular headlamp configurations don't illuminate the roads precisely, especially during curves and on unpredictable terrains. Hence there is a need for adaptive headlamps in automobiles that can prevent Troxler's effect on the drivers of the opposite vehicles while not compromising on the illumination of the road for the driver on-board. This paper explores various methodologies used in implementing adaptive headlamps and explore the scope for future work in this area of research. Also, the paper reviews vehicle detection algorithms as well as various vehicle mathematical models for headlamp control based on steering angles INDEX TERMS Adaptive Front light Systems (AFS)
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%.
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.
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