Bridging academia and industry: A comprehensive review of advances, gaps, and future directions of fault detection and diagnosis ( <scp>FDD</scp> ) systems in the chemical industry
Bibliographic record
Abstract
Abstract This review analyzes the evolution, current state, and future directions of fault detection and diagnosis (FDD) systems in the chemical industry, highlighting the challenges and opportunities associated with their development and implementation. A systematic review of FDD methodologies, including model‐based, data‐driven, hybrid, and AI‐driven approaches, was conducted to evaluate their strengths, limitations, and industrial applicability. While model‐based methods provide high interpretability, they struggle with scalability and complexity in large‐scale operations. Data‐driven techniques excel in handling nonlinear and complex processes but are limited by the need for large, high‐quality datasets. Hybrid and AI‐driven systems offer a combination of adaptability and scalability; however, they face computational and interpretability challenges. The study identifies significant barriers to the widespread adoption of intelligent FDD systems, including the complexity of chemical processes, real‐time processing demands, scalability issues, integration with legacy systems, economic constraints, and organizational resistance. Despite these challenges, emerging technologies such as IoT, big data analytics, and explainable AI (XAI) present promising opportunities to enhance fault detection accuracy, adaptability, and sustainability. The findings emphasize the importance of developing modular, scalable, and explainable FDD systems that can seamlessly integrate into existing industrial infrastructures. This review underscores the need for greater collaboration between academia and industry to align theoretical advancements with practical requirements, ensuring that FDD systems are both technically robust and industrially viable. By addressing these challenges and leveraging emerging technologies, FDD systems can play a pivotal role in driving safer, more efficient, and sustainable operations in the chemical industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".