Hybrid Fusion Paradigm in Advanced Process Monitoring: A Panoramic Review and Future Perspectives
Bibliographic record
Abstract
Process monitoring plays a vital role in ensuring safe, efficient, and sustainable industrial operations. While prior surveys have advanced the field, most are fragmented, focusing narrowly on either specific algorithms or application domains. This review provides a comprehensive and integrative perspective by classifying monitoring methods into three broad categories: data-driven, model-driven, and fusion-driven approaches. The distinctive contribution of this work lies in moving beyond conventional comparisons. First, we present a balanced taxonomy that clarifies the scope and overlaps of different approaches. Second, we highlight emerging directions underrepresented in earlier surveys, including reinforcement learning, graph-based methods, topological data analysis, Bayesian optimization, hybrid deep architectures, and advanced state observers. Third, we emphasize practical implementation aspects, covering uncertainty quantification strategies and the deployment of both commercial and open-source monitoring software platforms. Finally, we connect process monitoring to the future of autonomous operations within Industry 4.0 ecosystems, outlining challenges related to integration, scalability, and interpretability. By consolidating methodological advances, industrial platforms, and forward-looking challenges, this review provides researchers and practitioners with a clear roadmap for designing monitoring systems that are accurate, interpretable, and resilient, while identifying open questions that will guide the next generation of autonomous process industries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".