scholarly journals Sustainability Analysis of a ZnO-NaCl-Based Capacitor Using Accelerated Life Testing and an Intelligent Modeling Approach

2021 ◽  
Vol 13 (19) ◽  
pp. 10736
Author(s):  
Pardeep Kumar Sharma ◽  
Cherry Bhargava ◽  
Ketan Kotecha
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Performance Metrics ◽  
Remaining Useful Life ◽  
Accelerated Life Testing ◽  
Life Testing ◽  
Storage Element ◽  
Accelerated Life ◽  
Useful Life ◽  
Artificial Neural

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.

scholarly journals Reliability and Remaining Life Assessment of an Electronic Fuze Using Accelerated Life Testing

Micromachines ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 272
Author(s):  
Noor Muhammad ◽  
Zhigeng Fang ◽  
Syed Yaseen Shah ◽  
Daniyal Haider
Keyword(s):  
High Reliability ◽  
Storage Life ◽  
Remaining Useful Life ◽  
Accelerated Life Testing ◽  
Life Assessment ◽  
Weak Links ◽  
Life Testing ◽  
Accelerated Life ◽  
Useful Life ◽  
Mission Critical

An electronic fuze is a one-shot system that has a long storage life and high mission criticality. Fuzes are designed, developed, and tested for high reliability (over 99%) with a confidence level of more than 95%. The electronic circuit of a fuze is embedded in the fuze assembly, and thus is not visible. Go/NoGo fuze assembly mission critical testing does not provide prognostic information about electrical and electronic circuits and subtle causes of failure. Longer storage times and harsh conditions cause degradation at the component level. In order to calculate accrued damage due to storage and operational stresses, it is necessary to perform sample-based accelerated life testing after a certain time and estimate the remaining useful life of mission critical parts. Reliability studies of mechanical parts of such systems using nondestructive testing (NDT) have been performed, but a thorough investigation is missing with regards to the electronic parts. The objective of this study is to identify weak links and estimate the reliability and remaining useful life of electronic and detonating parts. Three critical components are identified in an electronic fuze circuit (1) a diode, (2) a capacitor, and (3) a squib or detonator. The accelerated test results reveal that after ten years of storage life, there is no significant degradation in active components while passive components need to be replaced. The squib has a remaining useful life (RUL) of more than ten years with reliability over 99%.

scholarly journals Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance

2019 ◽  
Vol 19 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Nykan Mirchi ◽  
Vincent Bissonnette ◽  
Nicole Ledwos ◽  
Alexander Winkler-Schwartz ◽  
Recai Yilmaz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Virtual Reality ◽  
Artificial Neural Networks ◽  
Performance Metrics ◽  
Anterior Cervical Discectomy ◽  
Relative Importance ◽  
Surgical Performance ◽  
Cervical Discectomy ◽  
Artificial Neural ◽  
Insight Into

Abstract BACKGROUND Virtual reality surgical simulators provide a safe environment for trainees to practice specific surgical scenarios and allow for self-guided learning. Artificial intelligence technology, including artificial neural networks, offers the potential to manipulate large datasets from simulators to gain insight into the importance of specific performance metrics during simulated operative tasks. OBJECTIVE To distinguish performance in a virtual reality-simulated anterior cervical discectomy scenario, uncover novel performance metrics, and gain insight into the relative importance of each metric using artificial neural networks. METHODS Twenty-one participants performed a simulated anterior cervical discectomy on the novel virtual reality Sim-Ortho simulator. Participants were divided into 3 groups, including 9 post-resident, 5 senior, and 7 junior participants. This study focused on the discectomy portion of the task. Data were recorded and manipulated to calculate metrics of performance for each participant. Neural networks were trained and tested and the relative importance of each metric was calculated. RESULTS A total of 369 metrics spanning 4 categories (safety, efficiency, motion, and cognition) were generated. An artificial neural network was trained on 16 selected metrics and tested, achieving a training accuracy of 100% and a testing accuracy of 83.3%. Network analysis identified safety metrics, including the number of contacts on spinal dura, as highly important. CONCLUSION Artificial neural networks classified 3 groups of participants based on expertise allowing insight into the relative importance of specific metrics of performance. This novel methodology aids in the understanding of which components of surgical performance predominantly contribute to expertise.

Artificial neural networks to assess the useful life of reinforced concrete elements deteriorated by accelerated chloride tests

2020 ◽  
Vol 31 ◽  
pp. 101445 ◽  
Author(s):  
J.M.P.Q. Delgado ◽  
F.A.N. Silva ◽  
A.C. Azevedo ◽  
D.F. Silva ◽  
R.L.B. Campello ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reinforced Concrete ◽  
Useful Life ◽  
Artificial Neural ◽  
Concrete Elements

scholarly journals Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology

2021 ◽  
Vol 21 (2) ◽  
pp. 1113
Author(s):  
Yosra Abdulaziz Mohammed ◽  
Eman Gadban Saleh
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Performance Metrics ◽  
Artificial Neural ◽  
Sensitivity Specificity ◽  
Receiver Operating

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver operating characteristic ROC).   Dataset was downloaded from UCI ml repository; it is composed of 9 attributes and 699 samples. The findings are clearly showing that the RBF NN classifier is the best in prediction of the type of breast tumors since it had recorded the highest performance in terms of correct classification rate (accuracy), sensitivity, specificity, and AUC (area under Receiver Operating Characteristic ROC) among all other models.</p>

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