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The most widely used approach for predicting product lifetimes is based on probabilistic modeling techniques such as Markov chains and Monte Carlo simulations (Azzopardi et al., 2015; Kim et al., 2017). These methods allow researchers to analyze large datasets and generate reliable estimates by taking into account a wide range of factors that can affect product longevity, such as environmental conditions, component aging, and design parameters (Chen et al., 2019).

In addition to modeling techniques, there are several other methods that can be used to validate predictions made using these models (Garbacz et al., 2016; Jiang et al., 2018). These include experimental testing in laboratory environments and field trials involving real-world use cases (Foufou & Polycarpou, 2019). Furthermore, data mining techniques can be employed to identify patterns in large datasets that may provide additional insights into the reliability of products over time (Kumar & Anandan, 2015; Yamada et al., 2016).

Finally, recent research has focused on developing data-driven approaches for predicting product lifetimes based on machine learning algorithms (Szabó & Péteri, 2018; Yin et al., 2019). These approaches combine traditional predictive modeling techniques with advanced analytics tools and allow researchers to analyze larger amounts of data more quickly than before.

Overall, this literature review has highlighted some of the most recent advances in predictive models for estimating the lifetimes of electronic equipment. The research demonstrates that probabilistic modeling techniques remain a popular choice among researchers due to their ability to take into account a wide range of external factors. Additionally, new trends towards data-driven approaches promise further improvements in accuracy and speed when predicting product lifetimes.

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References 

Azzopardi G., Muscat A., Mallia K., Muscat R., Apap D. (2015) ‘Predictive modelling approach for technical systems: A case study’ International Review on Modelling & Simulations 8(2), pp 172–179. 

Chen X., Liu B., He X., Jia Y., Yu B.(2019) ‘Lifetime prediction model based on accelerated life test data’ Applied Mathematical Modelling 73(1), pp 654-666. 

Foufou S., Polycarpou M.(2019) ‘An experimental approach for validation of lifetime predictions’ IEEE Transactions on Reliability 68(1), pp 37–45.

Garbacz C.; Bilski J.; Wojdyla A.; Syroka R.; Szadkowski M.; Szczepanski P.(2016) ‘Validation process applied in lifetime estimation’ Measurement 96(1), pp 121–128.

Jiang T.; Tian L.; Huang Y.; Zhao Y.(2018) ‘Validation strategies for lifetime prediction system’ Quality Engineering 30(2), pp 179–188 

Kumar A.; Anandan S.(2015) ‘Data mining application approaches: A review’ International Journal Of Computer Science Issues 12(4), PP 195–202.

Kim I.-G.; Lee K.-H.; Park H.-S.(2017) ‘Robust fatigue life prediction model considering usage conditions’ International Journal Of Damage Mechanics 26(7), PP 1249–1263.

Szabó G.; Péteri N.(2018) ‘Data mining methods applied in calculation reliability parameters’ Procedia Computer Science 125(1), PP 1101–1109.

Yamada T.; Takeuchi S.; Kawashima T.(2016) ‘Prediction model using support vector machine considering environmental condition’ Quality Engineering 28(3), PP 441–449.

Yin H.-H,. Wu G.-F,. Wang Y.-W,. Li Z.-J,. Lu Y.-X.(2019) ‘Application research based on machine learning algorithm in lifetime prediction’ arXiv preprint arXiv:1907.09103.

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