Component based reliability prediction

ChauPattnaik, Sampa; Ray, Mitrabinda; Nayak, Mitali Madhusmita

doi:10.1007/s13198-021-01079-x

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Deep analysis for sample separation often reveals relatively homogeneous characteristics in segments, which provides advantages for building processing models [19,20]. Investigated various aspects of vertically separated data, proposed techniques, basic algorithms, and combination strategies aimed at selecting observation objects [21,22]. This makes it possible to obtain the main characteristics of sequences and samples and exclude values that lead to distortion of properties [23].…”

Section: -Literature Reviewmentioning

confidence: 99%

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Lebedev,

Sukhoparov

2024

Emerg Sci J

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This paper presents a solution for building and implementing data processing models and experimentally evaluates new possibilities for improving ensemble methods based on multilevel data processing models. This study proposes a model to reduce the cost of retraining models when transforming data properties. The research objective is to improve the quality indicators of machine learning models when solving classification problems. The novelty is a method that uses a multilevel architecture of data processing models to determine the current data properties in segments at different levels and assign algorithms with the best quality indicators. This method differs from the known ones by using several model levels that analyze data properties and assign the best models to individual segments of data and training. The improvement consists of using unsupervised clustering of data samples. The resulting clusters are separate subsamples for assigning the best machine-learning models and algorithms. Experimental values of quality indicators for different classifiers on the whole sample and different segments were obtained. The findings show that unsupervised clustering using multilevel models can significantly improve the quality indicators of “weak” classifiers. The quality indicators of individual classifiers improve when the number of data clusters is increased to a certain threshold. The results obtained are applicable to classification when developing models and machine learning methods. The proposed method improved the classification quality indicators by 2–9% due to segmentation and the assignment of models with the best quality indicators in individual segments. Doi: 10.28991/ESJ-2024-08-01-025 Full Text: PDF

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Section: -Literature Reviewmentioning

confidence: 99%

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Lebedev,

Sukhoparov

2024

Emerg Sci J

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“…In the context of design-time reliability prediction, various models [ 7 , 13 , 19 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] have been developed for the early design stages. These models can be classified into gray-box [ 30 , 31 , 32 , 33 ] and white-box [ 7 , 13 , 19 , 25 , 26 , 27 , 28 ].…”

Section: Related Workmentioning

confidence: 99%

Design-Time Reliability Prediction Model for Component-Based Software Systems

Ali

Bashir

Hassan

et al. 2022

Sensors

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Software reliability is prioritised as the most critical quality attribute. Reliability prediction models participate in the prevention of software failures which can cause vital events and disastrous consequences in safety-critical applications or even in businesses. Predicting reliability during design allows software developers to avoid potential design problems, which can otherwise result in reconstructing an entire system when discovered at later stages of the software development life-cycle. Several reliability models have been built to predict reliability during software development. However, several issues still exist in these models. Current models suffer from a scalability issue referred to as the modeling of large systems. The scalability solutions usually come at a high computational cost, requiring solutions. Secondly, consideration of the nature of concurrent applications in reliability prediction is another issue. We propose a reliability prediction model that enhances scalability by introducing a system-level scenario synthesis mechanism that mitigates complexity. Additionally, the proposed model supports modeling of the nature of concurrent applications through adaption of formal statistical distribution toward scenario combination. The proposed model was evaluated using sensors-based case studies. The experimental results show the effectiveness of the proposed model from the view of computational cost reduction compared to similar models. This reduction is the main parameter for scalability enhancement. In addition, the presented work can enable system developers to know up to which load their system will be reliable via observation of the reliability value in several running scenarios.

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“…Classical and Bayesian prediction in the Bur III model were discussed by Singh et al (2019). ChauPattnaik et al (2021) discuss component based reliability prediction using Markov chains techniques. Prediction of remaining useful life in some distributions was investigated using Artificial neural networks (ANN) by Farsi and Hosseini (2019).…”

Section: Introductionmentioning

confidence: 99%

Prediction of future failures in the log-logistic distribution based on hybrid censored data

Ghaida

Baklizi

2022

Int J Syst Assur Eng Manag

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We consider the prediction of future observations from the log-logistic distribution. The data is assumed hybrid right censored with possible left censoring. Different point predictors were derived. Specifically, we obtained the best unbiased, the conditional median, and the maximum likelihood predictors. Prediction intervals were derived using suitable pivotal quantities and intervals based on the highest density. We conducted a simulation study to compare the point and interval predictors. It is found that the point predictor BUP and the prediction interval HDI have the best overall performance. An illustrative example based on real data is given.

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Component based reliability prediction

Cited by 4 publications

References 17 publications

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Design-Time Reliability Prediction Model for Component-Based Software Systems

Prediction of future failures in the log-logistic distribution based on hybrid censored data

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