Impact of ambient temperature set point deviation on Arrhenius estimates

Whitman, Charles S.

doi:10.1016/j.microrel.2011.09.023

Cited by 8 publications

(7 citation statements)

References 3 publications

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“…As proposed by Whitman (2012), derating of capacitor life and its performance degradation accelerates with increased temperature. As the temperature increases, it initiates a change in chemical reactions because an electrolytic capacitor is an electrochemical device (Rusdi et al, 2005).…”

Section: Lifetime Calculation Using Method-amentioning

confidence: 99%

Condition monitoring of aluminium electrolytic capacitors using accelerated life testing

Bhargava

Banga

Singh

2018

IJQRM

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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.

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Section: Lifetime Calculation Using Method-amentioning

confidence: 99%

Condition monitoring of aluminium electrolytic capacitors using accelerated life testing

Bhargava

Banga

Singh

2018

IJQRM

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“…Using Minitab 18.1 software, regression analysis has been done ( Figure 7 and Table 4 Using equation (17), lifetime is calculated using a regression equation.…”

Section: Residual Life Calculation Using a Statistical Methodsmentioning

confidence: 99%

“…The Student's t-test is used to test the accuracy of the model. Accuracy can be predicted by the fact that variance value should be minimal and R 2 value should be higher 27 Lifetime = À58, 782 ð Þ+ 2352:6 3 Temperature ð Þ Using equation (17), lifetime is calculated using a regression equation.…”

Section: Residual Life Calculation Using a Statistical Methodsmentioning

confidence: 99%

“…With the increase in thermal stress, the derating and degradation of electrolytic capacitor accelerates, as proposed by CS Whitman. 17 As an electrolytic capacitor is an electrochemical device, the chemical reactions trigger with the increase in temperature. R Ko¨tz et al 18 have explored that temperature is a critical parameter for evaluating performance of capacitors.…”

Section: Proposed Model For Residual Life Calculationmentioning

confidence: 99%

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

Bhargava

Banga²,

Singh

2018

Advances in Mechanical Engineering

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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%.

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“…through the capacitor seal and the equivalent series resistance that is a measure of electrolyte loss also changes with change in temperature [13]. So, the higher temperature changes affect the lifetime of a capacitor [14].…”

Section: A Critical Parameters Affecting Life Of Capacitormentioning

confidence: 99%

DSSCC: Decision Support System for Ceramic Capacitor using Fuzzy Logic

Bhargava¹,

Gulati²,

Sharma³

2019

IJITEE

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As the technology advances, the reliability becomes the main constraint for the successful operation of the electronic product. To fully automate the system, the electronic devices become more and more complex. Reliability becomes a challenge with the regular demand of low cost and high-speed devices. Residual life estimation of passive devices such as resistor, capacitor etc. is of a great concern. Failure of one small component can lead to fully damage of whole system. In this paper, a practical approach i.e. accelerated life testing is deployed to calculate remaining useful life of the ceramic capacitor. An intelligent model is formulated using various artificial intelligence techniques. Artificial Neural Network (ANN), Fuzzy Inference System (FIS) as well as Adaptive Neuro Fuzzy Inference System (ANFIS) are deployed to predict the remaining useful lifetime of an electrolytic capacitor. An error analysis is conducted to estimate the most accurate intelligent technique. A fuzzy based decision support system is modelled, which provides an interactive GUI to users. The user can access the live health status of electrolytic capacitor at various input parameters. It will warn the user to replace or repair the upcoming fault in the component or device, before it actually degrades or shut downs the complete system. The comparative analysis of all the artificial intelligence techniques shows that Adaptive Neuro Fuzzy Inference System (ANFIS) has the highest accuracy i.e. 99.5%, as compare to Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), where accuracy rate is 98.06% and 97.84% respectively. This prediction system is helpful to reduce the problem of electronic e-waste by enabling the user to reuse the component

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Impact of ambient temperature set point deviation on Arrhenius estimates

Cited by 8 publications

References 3 publications

Condition monitoring of aluminium electrolytic capacitors using accelerated life testing

Condition monitoring of aluminium electrolytic capacitors using accelerated life testing

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

DSSCC: Decision Support System for Ceramic Capacitor using Fuzzy Logic

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