Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning?

Gámiz, María Luz; Navas-Gómez, Fernando; Nozal Cañadas, Rafael Adolfo; Raya-Miranda, Rocío

doi:10.3390/machines12120909

Open AccessArticle

Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning?

by

María Luz Gámiz

¹

,

Fernando Navas-Gómez

¹

,

Rafael Adolfo Nozal Cañadas

²

and

Rocío Raya-Miranda

^1,*

¹

Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain

²

Department of Computer Science, UiT The Arctic University of Norway, 9037 Tromsø, Norway

^*

Author to whom correspondence should be addressed.

Machines 2024, 12(12), 909; https://doi.org/10.3390/machines12120909

Submission received: 13 November 2024 / Revised: 5 December 2024 / Accepted: 9 December 2024 / Published: 11 December 2024

(This article belongs to the Special Issue State of the Art in the Field of Machine and System Testing to Assist in the Diagnosis and Prognosis of Failures)

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Abstract

Studying the reliability of complex systems using machine learning techniques involves facing a series of technical and practical challenges, ranging from the intrinsic nature of the system and data to the difficulties in modeling and effectively deploying models in real-world scenarios. This study compares the effectiveness of classical statistical techniques and machine learning methods for improving complex system analysis in reliability assessments. Our goal is to show that in many practical applications, traditional statistical algorithms frequently produce more accurate and interpretable results compared with black-box machine learning methods. The evaluation is conducted using both real-world data and simulated scenarios. We report the results obtained from statistical modeling algorithms, as well as from machine learning methods including neural networks, K-nearest neighbors, and random forests.

Keywords: logistic regression; factorial analysis; isotonic smoothing; machine learning; supervised learning; unsupervised learning; ANN; KNN; RF

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MDPI and ACS Style

Gámiz, M.L.; Navas-Gómez, F.; Nozal Cañadas, R.A.; Raya-Miranda, R. Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning? Machines 2024, 12, 909. https://doi.org/10.3390/machines12120909

AMA Style

Gámiz ML, Navas-Gómez F, Nozal Cañadas RA, Raya-Miranda R. Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning? Machines. 2024; 12(12):909. https://doi.org/10.3390/machines12120909

Chicago/Turabian Style

Gámiz, María Luz, Fernando Navas-Gómez, Rafael Adolfo Nozal Cañadas, and Rocío Raya-Miranda. 2024. "Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning?" Machines 12, no. 12: 909. https://doi.org/10.3390/machines12120909

APA Style

Gámiz, M. L., Navas-Gómez, F., Nozal Cañadas, R. A., & Raya-Miranda, R. (2024). Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning? Machines, 12(12), 909. https://doi.org/10.3390/machines12120909

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning?

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