High temperature reliability testing of aluminum and tantalum electrolytic capacitors

Dehbi, A.; Wondrak, W.; Ousten, Yves; Danto, Yves

doi:10.1016/s0026-2714(02)00021-5

Cited by 39 publications

(16 citation statements)

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“…After comparing two different approaches of lifetime prediction with experimental data it has been noted that the method proposed in [5] provides much more accurate results than the one proposed in [4], which had an error rate of over 200%.…”

Section: Discussionmentioning

confidence: 99%

“…It is a well-accepted fact in electronics that capacitors, especially aluminum and tantalum electrolytic ones, are critical components and temperatures reaching 100 o C and higher [4].…”

Section: Predicting Capacitor Failuresmentioning

confidence: 99%

“…al. in [4]. Using equation (1) the capacitor lifetime can be approximated: As it can be seen in (1) the operating voltage has an effect only on radial capacitor lifetimes.…”

Section: Predicting Capacitor Failuresmentioning

confidence: 99%

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Parameter monitoring of electronic circuits for reliability prediction and failure analysis

Jano

Pitica

2011

Proceedings of the 2011 34th International Spring Seminar on Electronics Technology (ISSE)

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As electronic modules are becoming ever more popular in critical safety applications, such as electronic vehicles and aeronautics, so do their operating conditions become harsher. This sometimes means using electronic components at the very limit of their functional parameters and very close to or even exceeding their maximum operating temperature. This of course leads to a dramatic decrease in component lifetimes and classical failure prediction algorithms are thrown off balance, giving unreliable results.This of course means that new prediction methods should be established. The following paper proposes a method for predicting the value of capacitors on the electronic modules, in order to perform real-time prediction of their lifetimes affected by their operational environment. First, two capacitor lifetime models are presented and compared and then the theoretical data are compared with the data obtained from accelerated ageing measurements.

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Section: Discussionmentioning

confidence: 99%

“…It is a well-accepted fact in electronics that capacitors, especially aluminum and tantalum electrolytic ones, are critical components and temperatures reaching 100 o C and higher [4].…”

Section: Predicting Capacitor Failuresmentioning

confidence: 99%

See 1 more Smart Citation

Parameter monitoring of electronic circuits for reliability prediction and failure analysis

Jano

Pitica

2011

Proceedings of the 2011 34th International Spring Seminar on Electronics Technology (ISSE)

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“…Li Bo, et al [6] researched the degradation trend of capacitance (C ) and dissipation factor (DF ) of the tantalum capacitor from using temperatures of 25-205 • C. The experimental results showed that the capacitance C was approximately linear to the temperature and C/C < 12%, and the DF increased with fluctuation in the degradation process. Dehbi, et al [7] found that the capacitance changed little for 1000 h at 25, 85, and 125 • C, while the change at 150 • C was 2-3 times that at 25 • C. Deloffre, et al [8] studied the relationship between capacitance and voltage of the tantalum capacitor in lowtemperature stress of 5-300 k. The capacitance could increase linearly with increasing temperature. Peter et al [9] proposed that the capacitance and dissipation factors of a tantalum capacitor with MnO 2 compared to those with polymer have different degradation degrees at 95 • C and 70% relative humidity (RH).…”

Section: Introductionmentioning

confidence: 99%

Ordered clustering method and degradation trend analysis for performance degradation of tantalum capacitor

Zhao

Liu

2019

IEEJ Transactions Elec Engng

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The performance degradation trend of a tantalum capacitor has different characteristics in different degradation regimes with different temperatures and humidity. Clustering method and degradation trend analysis can be useful for early warning of life. In this paper, according to the degradation characteristics of the capacitance and the dissipation factor with different temperature and humidity, the experiment is designed, and the failure mechanisms of different degeneration regimes are analyzed. After the ordered clustering method is introduced, using the natural property and the change rate α(k ) of the slope β(k ) of the error function δ[p(m,k )], the determination method of the optimization clustering number is put forward. The Davies-Bouldin Index is proposed as the evaluation method of the ordered clustering result, and the validation of the evaluation method was verified by the degradation data with other stresses. For the stable degradation regime, the degradation trend analysis is completed. The error result shows that the degradation fitting functions can be a good fit for the degradation trend with different stress. According to the degradation fitting analysis, the degradation prediction method is described to find the relationship between prediction life and different temperature and humidity to realize the failure time prediction with different stresses using the early degradation data.

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“…Except these factors such as temperature i.e. ambient temperature is also effective to the working period of ceramic capacitors [2]. In this paper the experimental observation on ceramic capacitor deterioration under various operating conditions is discussed and various experimental and intelligent techniques are deployed for health prognostics.…”

Section: Introductionmentioning

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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High temperature reliability testing of aluminum and tantalum electrolytic capacitors

Cited by 39 publications

References 0 publications

Parameter monitoring of electronic circuits for reliability prediction and failure analysis

Parameter monitoring of electronic circuits for reliability prediction and failure analysis

Ordered clustering method and degradation trend analysis for performance degradation of tantalum capacitor

DSSCC: Decision Support System for Ceramic Capacitor using Fuzzy Logic

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