Condition monitoring of aluminium electrolytic capacitors using accelerated life testing

Bhargava, Cherry; Banga, Vijay Kumar; Singh, Yaduvir

doi:10.1108/ijqrm-06-2017-0115

Cited by 12 publications

(14 citation statements)

References 12 publications

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“…A sample adequacy was estimated using “ Kaiser – Meyer– Olkin ” (KMO) measure (Bhargava et al. , 2018a, b; Nakic Rados et al. , 2022).…”

Section: Resultsmentioning

confidence: 99%

“…, 2000; Kline, 2013; Bhargava et al. , 2018a, b; Jaggi and Nim, 2020). Item difficulty (ID) indicates the ratio of respondents who responds to an item correctly.…”

Section: Methodsmentioning

confidence: 99%

“…(2017), Bhargava et al. (2018a, b) and Han et al. (2019, p. 2), “ Face validity refers to how relevant and appropriate respondents consider questionnaire items ”.…”

Section: Methodsmentioning

confidence: 99%

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

Kansra

Oberoi

Bhargava

et al. 2022

IJQRM

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PurposeAccessibility to a precise tool for healthcare management and self-precaution among diabetic patients is an absolute necessity. This paper aims to develop and validate diabetes-related awareness instrument (DRAI) – an instrument that measures diabetics awareness about risk factors and prevention strategies.Design/methodology/approachThe reliability and validity of the DRAI were tested with a sample of 112 diabetics. The construct validity of the DRAI was measured using exploratory and confirmatory factor analysis. Item discrimination, reliability, usefulness and validity of the items were determined by performing Cronbach's alpha, item difficulty and discrimination index analysis.FindingsThe study finds DRAI – a reliable and valid instrument to assess diabetics awareness towards diabetes mellitus, its associated risk factors and prevention strategies. The value of Cronbach's alpha for all three constructs was above the threshold level of 0.70. Under exploratory factor analysis, “Kaiser–Meyer–Olkin” test value of 0.805 exhibits a meritorious sample adequacy and “Bartlet's test of Sphericity” was statistically significant with p = 0.032. Therefore, results of confirmatory factor analysis (CFA) revealed that all fitness indices of the model to be excellent fit.Practical implicationsThe present instrument can help to determine whether the individual is susceptible to diabetes, timely prevention and reduction in the incidence of diabetes mellitus.Originality/valueDRAI is the first of its kind tool to assess the awareness and knowledge about diabetes-related risk factors and prevention strategies in such a demographically diverse population of India.

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“…A sample adequacy was estimated using “ Kaiser – Meyer– Olkin ” (KMO) measure (Bhargava et al. , 2018a, b; Nakic Rados et al. , 2022).…”

Section: Resultsmentioning

confidence: 99%

“…, 2000; Kline, 2013; Bhargava et al. , 2018a, b; Jaggi and Nim, 2020). Item difficulty (ID) indicates the ratio of respondents who responds to an item correctly.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Kansra

Oberoi

Bhargava

et al. 2022

IJQRM

Self Cite

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“…The reasons for failure of electrolytic capacitors are wide, some are enlisted as following [ Figure 2. is also known as fishbone diagram, as it describes the causes of failure as well as its effect on the performance as well as life of the components in use [14]. There are so many consequences related with the degradation of electrolytic capacitor.…”

Section: A Failure Modes Of An Electrolytic Capacitormentioning

confidence: 99%

Estimation of Residual Lifetime of Electrolytic Capacitor using Analytical Techniques

Bhargava¹,

Gulati²,

Sharma³

2019

IJRTE

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In the fast-evolving era of digital electronics, reliability has become a critical issue. Due to failure and faults, the component manufacturers face market reputation degradation as well as financial set back. The condition monitoring for power electronics are based on basic principles; however, by analyzing failure modes of semiconductor devices and exploring appropriate techniques, situation has been improved a lot. From toy to satellite, an electrolytic capacitor is mostly used as an important component. This paper enlightens the estimation of residual lifetime of electrolytic capacitor using analytical techniques, so that these components can be reused and problem of WEEE (Waste of Electrical and Electronic Equipment) can be reduced to a large extent. The residual life of electrolytic capacitor is calculated using various analytical and mathematical technique. Accuracy of empirical standard prediction methods such as military handbook MILHDBK and RIAC217 is compared for the residual life prediction of electrolytic capacitor. RIAC217 plus technique proves to be more accurate than MILHDBK or other standard techniques. By predicting the residual life, the capability of re-use the component increases and problem of e-waste is decreased to a great extent. Thus, the residual lifetime prediction is a critical parameter for successful operation of device as well as safe healthy environment

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“…Rodrigues et al (2020) used particle filter heuristic to predict the RUL of lithium batteries with the presence of various degradation factors. For monitoring the condition of the equipment, Bhargava (2018) used the accelerated aging due to temperature stress to analyze the conditions of the aluminum electrolytic capacitor.…”

Section: Introductionmentioning

confidence: 99%

Detection and Fault Prediction in Electrolytic Capacitors Using Artificial Neural Networks

Mesquita

Pinto

Rodrigues

2021

Communications in Computer and Information Science

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Capacitors are electronic components that present a considerable variation in their characteristics during their useful life. After being submitted to several charge/discharge cycles, capacitors present losses in capacitance values and operate differently from the nominal characteristics. PHM (Prognostics and Health Monitoring) techniques can be used to monitor the evolution of a capacitor health condition and to predict its RUL (Remaining Useful Life). This paper uses artificial neural networks to monitor the degradation index of capacitors and predict the corresponding RUL. Different neural network architectures are investigated: MLP (Multilayer Perceptron), RBF (Radial Basis Function), and ELM (Extreme Learning Machine). The performances of the different architectures are compared in terms of the coefficient of determination (R 2 ) and the Mean Squared Error (MSE). The accuracy of RUL predictions are compared based on the Relative Accuracy (RA) indicator, which is a performance indicator proposed in the literature to evaluate PHM algorithms.

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Condition monitoring of aluminium electrolytic capacitors using accelerated life testing

Cited by 12 publications

References 12 publications

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

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

Estimation of Residual Lifetime of Electrolytic Capacitor using Analytical Techniques

Detection and Fault Prediction in Electrolytic Capacitors Using Artificial Neural Networks

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