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This is a Quantitative Study study.

October 3, 2025Energy and automation

Using machine learning algorithms for diagnostics of moving equipment control systems

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Authors

OTOleh J. TretiakPurdue University West Lafayette

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Implication

Research demonstrates the effectiveness of machine learning algorithms for predictive diagnostics in control systems, highlighting key advantages over classical methods.

Key Points

High accuracy in diagnostics is achievable with machine learning, with LSTM models predicting failures at 95% accuracy.
A comparative analysis showed that deep neural networks outperform classical methods in adapting to new data and resisting noise.
Automated solutions for monitoring equipment technical conditions are vital for modern industrial production and transport sectors.
Major challenges include the need for large training datasets and maintaining energy efficiency during real-world implementation.

Cite This Study

Oleh J. Tretiak (2025) studied this question.

synapsesocial.com/papers/68e02f2cf0e39f13e7fa1d8b https://doi.org/10.31548/energiya1(77).2025.069

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Implication

Research demonstrates the effectiveness of machine learning algorithms for predictive diagnostics in control systems, highlighting key advantages over classical methods.

Using machine learning algorithms for diagnostics of moving equipment control systems

Discussion

Member takes

Implication

Key Points

Cite This Study

Using machine learning algorithms for diagnostics of moving equipment control systems

Discussion

Member takes

Implication

Key Points

Cite This Study

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