Application of Hadoop in Data Science

Singh, Balraj; Verma, Harsh Kumar

doi:10.1002/9781119776499.ch9

Cited by 2 publications

(1 citation statement)

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“…The choice of algorithm depends on factors such as the nature and complexity of the problem, the size and quality of the avail-able data, computational requirements, interpretability of the model, and specific performance requirements. Each algorithm has its strengths and limitations, and the selection should be informed by the unique characteristics and needs of the proactive maintenance system, ensuring that the chosen models align with the overarching objectives while delivering accurate and actionable insights for fault prediction and preventive maintenance operations [7] [9] [10].…”

Section: Model Developmentmentioning

confidence: 99%

Proactive System Maintenance: Machine Learning Models for Predicting and Preventing Potential System Failures

Kumar

2024

IJRASET

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In modern industrial systems, the prevention of failures and downtime is of paramount importance for ensuring efficiency and productivity. Proactive system maintenance approaches leverage machine learning (ML) models to predict potential failures before they occur, enabling pre-emptive actions to be taken [1]. In this paper, we present a comprehensive review of existing research on proactive system maintenance, focusing on the development and application of ML algorithms for fault prediction and prevention. We discuss various machine learning techniques, data sources, feature engineering methods, and evaluation metrics employed in this domain [8]. Furthermore, we propose novel algorithms and strategies for enhancing the effectiveness of proactive maintenance systems. Through experimentation and case studies, we demonstrate the feasibility and benefits of utilizing machine learning for proactive maintenance in diverse industrial settings.

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Section: Model Developmentmentioning

confidence: 99%