← Back to Research Radar
Academic Publication Academic Publication

Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions

77
Citations
April 28, 2025
Published Date

Research Abstract & Technology Focus

This systematic literature review (SLR) provides a comprehensive application-wise analysis of machine learning (ML)-driven predictive maintenance (PdM) across industrial domains. Motivated by the digital transformation of industry 4.0, this study explores how ML techniques optimize maintenance by predicting faults, estimating remaining useful life (RUL), and reducing operational downtime. Sixty peer-reviewed articles published between 2020 and 2024 were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, and were analyzed based on industrial sector, ML techniques, datasets, evaluation metrics, and implementation challenges. Results show that combining ML with diverse sensor data enhances predictive performance under varying operational conditions across manufacturing, energy, healthcare, and transportation. Frequently used open datasets include the commercial modular aero-propulsion system simulation (CMAPSS), the malfunctioning industrial machine investigation and inspection (MIMII), and the semiconductor manufacturing process (SECOM) datasets, though data heterogeneity and imbalance remain major barriers. Emerging paradigms such as hybrid modeling, digital twins, and physics-informed learning show promise but face issues like computational cost, interpretability, and limited scalability. The findings highlight future research needs in model generalizability, real-world validation, and explainable artificial intelligence (AI) to bridge gaps between ML innovations and industrial practice.
application-wise review machine learning-based predictive maintenance
Read Full Literature

Correlated Market Trend: Adversarial Review

Bridging academia to market: The 60-day public search velocity mapping directly to the core technology of this paper. Dashed line represents 7-day moving average.

AI Semantic Synergy Context

Connecting this academic literature to real-world market discussions and products.

crossref.org › academic paper
75%
🔥

Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions

This systematic literature review (SLR) provides a comprehensive application-wise analysis of machine learning (ML)-driven predictive maintenance (PdM) across industrial domains. Motivated by the d...
crossref.org › academic paper
12%

Condition Monitoring and Predictive Maintenance in Industrial Equipment: An NLP-Assisted Review of Signal Processing, Hybrid Models, and Implementation Challenges

Failures in critical industrial components (bearings, compressors, and conveyor belts) often lead to unplanned downtime, high costs, and safety concerns. Traditional diagnostic approaches underperf...
crossref.org › academic paper
0%

AI-Driven Predictive Maintenance in Mining: A Systematic Literature Review on Fault Detection, Digital Twins, and Intelligent Asset Management

The mining industry faces increasing challenges in maintaining high production levels while minimizing unplanned failures and operational costs. Critical assets, such as crushers, conveyor belts, m...
crossref.org › academic paper
0%

Machine Learning for Predictive Analytics: Trends and Future Directions

Machine Learning has become an integral part of predictive analysis, empowering organizations to identify and analyze trends, uncover patterns, and make data-driven decisions across diverse domains...
crossref.org › academic paper
0%

Dynamic predictive maintenance strategy for system remaining useful life prediction via deep learning ensemble method

No description provided.

Frequently Asked Questions (FAQ)

Curated market intelligence mapped to this research.

What is the core focus of the research titled 'Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions'?

This literature focuses on: This systematic literature review (SLR) provides a comprehensive application-wise analysis of machine learning (ML)-driven predictive maintenance (PdM) across industrial domains. Motivated by the digital transformation of industry 4.0, this study ...

Are there open-source GitHub repositories related to Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions?

Yes, open-source projects like wanshuiyin/Auto-claude-code-research-in-sleep (ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and exper...) are actively building upon these concepts.

Which startups are commercializing the technology behind Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions?

Products like Superset are bringing this to market. Their focus is: Run an army of Claude Code, Codex, etc. on your machine.

What other academic literature is closely related to 'Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions'?

Yes, highly correlated activity was mapped. An entry titled 'Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions' discusses this: This systematic literature review (SLR) provides a comprehensive application-wise analysis of machine learning (ML)-driven predictive maintenance (...

Cite this Market Intelligence Report

Reference our AI-mapped synergy between this research and the commercial market to instantly build authority.

Commercial Realization

Startups and Open Source tools heavily associated with the concepts explored in this paper.

Associated Media Narrative